Publications

2026

  • F Levrero-Florencio, Y Lee, J Pathak, GE Karniadakis. 2026. "NSPOD: Accelerating Krylov solvers via DeepONet-learned POD subspaces." ArXiv preprint arXiv:2605.07828. 
  • Z Gao, GE Karniadakis. 2026. "Safe Cross-Entropy importance sampling for extremely small failure probabilities." Reliability Engineering & System Safety, 276, 112839. doi:10.1016/j.ress.2026.112839
  • R Wan, GE Karniadakis, P Stinis. 2026. "From LIF to QIF: Toward differentiable spiking neurons for scientific machine learning." npj Artif. Intell. doi:10.1038/s44387-026-00121-2
  • Srivastava A and DM Tartakovsky. 2026. "Computable kinetic defect measure for scalar hyperbolic conservation laws." Journal of Computational Physics 564, 115181. doi:10.1016/j.jcp.2026.115181.
  • Yang CF, Y Cui, and DM Tartakovsky. 2026. "Universality class of ion-intercalation models." Journal of Physical Chemistry Letters 17(25), 7154-7158. doi:10.1021/acs.jpclett.6c01670.
  • Wildt N, DM Tartakovsky, S Oladyshkin, and W Nowak. 2026. "CODE: A global approach to ODE dynamics learning." Journal of Machine Learning for Modeling and Computing 7(2), 73-105. doi:10.1615/JMachLearnModelComput.2026062518.
  • Chen W., A.A. Howard, and P. Stinis. 2026. "Self-adaptive weighting and sampling for physics-informed neural networks." Machine Learning: Science and Technology 7:025050. doi:10.1088/2632-2153/ae556e.
  • Zhao H and DM Tartakovsky. 2026. "Structure-aware initialization via numerical continuation and informed priors." Computing in Science & Engineering. doi:10.1109/MCSE.2026.3681730.
  • Monteiro HBN and DM Tartakovsky. 2026. "Integral kernel methods for nonlinear parabolic-elliptic systems." SIAM Journal on Scientific Computing 48(2), A958-A983. doi:10.1137/25M1732507.
  • Propp AM, JA Actor, E Walker, H Owhadi, N Trask, and DM Tartakovsky. 2026. "Discovery of probabilistic Dirichlet-to-Neumann maps on graphs." SIAM Journal on Scientific Computing 48(2), C191-C215. doi:10.1137/25M1765201.
  • Zhang Y and DM Tartakovsky. 2026. "Regional surrogates for predictive control of digital twins." Journal of Computational Physics 557, 114846. doi:10.1016/j.jcp.2026.114846.
  • Yang X, Y Cui, and DM Tartakovsky. 2026. "Electrochemical models of multi-unit lithium batteries under thermal gradient." Journal of the Electrochemical Society 173(5), 050520. doi:10.1149/1945-7111/ae4b6d.
  • Viswanathan AS, A Bock, Z Bent, MA Peyton, DM Tartakovsky, and JE Santos. 2026. "Two-stage wildlife event classification for edge deployment." Sensors 26(4), 1366. doi:10.3390/s26041366.
  • Viswanathan A, X Yang, and DM Tartakovsky. 2026. "Fourier neural operator surrogate of lithium-ion battery models." Journal of Machine Learning for Modeling and Computing 7(1), 51-68. doi:10.1615/JMachLearnModelComput.2026063142.
  • Yang X and DM Tartakovsky. 2026. "Estimation of battery core temperature from indirect observations and models with error." Journal of Power Sources 664, 238948. doi:10.1016/j.jpowsour.2025.238948.
  • Li G, I Xiu, and DM Tartakovsky. 2026. "Gaussian-process models of population dynamics." Journal of Machine Learning for Modeling and Computing 7(1), 25-49. doi:10.1615/JMachLearnModelComput.2025061979.
  • Jiang S, A Voronin, EC Cyr, and BS Southworth. 2026. "On the convergence behavior of preconditioned gradient descent toward the rich learning regime." arXiv preprint arXiv:2601.03162. (ICLR 2026)
  • Vijaywargia A, EC Cyr, and A Gruber. 2026. "Structure-aware tensorial model reduction." arXiv preprint arXiv:2604.26280. (submitted to CMAME)
  • Actor JA, G Harper, BS Southworth, and EC Cyr. 2026. "Multilevel training for Kolmogorov-Arnold networks." arXiv preprint arXiv:2603.04827. (submitted to SISC)
  • Lee D, A Moitra, Y Kim, R Yin, and P Panda. 2026. "MD-SNN: Membrane potential-aware distillation on quantized spiking neural network." 2026 Design, Automation & Test in Europe Conference (DATE), 1-7.
  • Shaffer B, P Hsieh, B Kinch, N Trask, and M Hsieh. 2026. "Neural navigation functions for zero-shot generalizable motion planning." arXiv preprint arXiv:2606.03756.
  • Zhang H, A Propp, B Kinch, H Owhadi, and N Trask. 2026. "Structure-preserving neural surrogates with tractable uncertainty quantification." arXiv preprint arXiv:2606.11650.
  • Shaffer B, B Kinch, M Hsieh, and N Trask. 2026. "A meshfree exterior calculus for generalizable and data-efficient learning of physics from point clouds." arXiv preprint arXiv:2605.08436. (submitted to NeurIPS)
  • Kinch B, X Hu, Y Huang, M Hansen, S Meltzer, N Hamlin, D Sirajuddin, EC Cyr, and N Trask. 2026. "A hybridizable neural time integrator for stable autoregressive forecasting." arXiv preprint arXiv:2604.21101. (submitted to Nature Computational Science)
  • Voronin A, E Walker, T Bourdais, H Owhadi, and N Trask. 2026. "DG2DAG: Learning directed acyclic graphs from functional priors." (submitted to NeurIPS)
  • Sankaran S, W Ortiz, D Bolintineanu, S Wang, R Rao, P Perdikaris, and N Trask. 2026. "Learning unconventional rheology: Multi-experiment fusion with physics-informed neural networks." Computers & Fluids, 107037. doi:10.1016/j.compfluid.2026.107037
  • Shaffer B, S Koohy, B Kinch, M Hsieh, and N Trask. 2026. "Structure-preserving learning improves geometry generalization in neural PDEs." arXiv preprint arXiv:2602.02788. (ICML 2026)
  • Yang X, M Darcy, M Hudes, FJ Alexander, G Eyink, and H Owhadi. 2026. "Solving functional PDEs with Gaussian processes and applications to functional renormalization group equations." Journal of Computational Physics 563, 115085. doi:10.1016/j.jcp.2026.115085
  • Baptista R, E Calvello, M Darcy, H Owhadi, AM Stuart, and X Yang. 2026. "Solving roughly forced nonlinear PDEs via misspecified kernel methods and neural networks." Mathematics of Computation. doi:10.1090/mcom/4219
  • Yang X and H Owhadi. 2026. "A minibatch method for solving nonlinear PDEs with Gaussian processes." SIAM Journal on Scientific Computing 48(4), C635-C657. doi:10.1137/23M1576372
  • Zou Z, T Bourdais, R Baptista, and H Owhadi. 2026. "Conditional generative modeling for digital twin modeling." arXiv preprint arXiv:2606.16219.
  • Bacho A, J Lee, and H Owhadi. 2026. "KROM: Kernelized reduced order modeling." arXiv preprint arXiv:2603.00360.
  • Sharma R, M Lowery, H Owhadi, and V Shankar. 2026. "Fluids you can trust: Property-preserving operator learning for incompressible flows." arXiv preprint arXiv:2602.15472.
  • V Kumar, GE Karniadakis. 2026. "Agentic Risk-Aware Set-Based Engineering Design." ArXiv preprint arXiv:2604.16687.
  • JD Toscano, Z Chai, and GE Karniadakis. 2026. "GRAFT-ATHENA: Self-Improving Agentic Teams for Autonomous Discovery and Evolutionary Numerical Algorithms." ArXiv preprint arXiv:2605.11117. 
  • Z Gao, GE Karniadakis. 2026. "Proposal-Guided Greedy Surrogate Refinement for PDE-Driven High-Dimensional Rare-Event Estimation." ArXiv preprint arXiv:2605.15356.
  • Y Liu, H Xu, Y He, SR Patil, M Xu, and T Ma. 2026. "HyperGuide: Hyperbolic Guidance for Efficient Multi-Step Reasoning in Large Language Models." ArXiv preprint arXiv:2605.24140.
  • Lee Y, A Kopaničáková, GE Karniadakis. 2026. “Two-level overlapping additive Schwarz preconditioner for training scientific machine learning applications.” Computer Methods in Applied Mechanics and Engineering 448, 118400. doi:10.1016/j.cma.2025.118400 
  • Khodakarami S, V Oommen, A Bora, GE Karniadakis. 2026. “Mitigating spectral bias in neural operators via high-frequency scaling for physical systems.” Neural Networks 193: 108027. doi:10.1016/j.neunet.2025.108027
  • Toscano JD, DT Chen, V Oommen, GE Karniadakis. 2026. "A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning." npj Artificial Intelligence, 2, 32. doi: 10.1038/s44387-026-00084-4.
  • Oommen V, S Khodakarami, A Bora, Z Wang, GE Karniadakis. 2026. "Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction." Nature Communications, 17:3707. doi:10.1038/s41467-026-70145-4.
  • Zhang S, LR Leung, BE Harrop, A Bora, G Karniadakis, K Shukla, K Zhang. 2026. "Improving Thermodynamic Nudging in the E3SM Atmosphere Model Version 2 (EAMv2): Strategy and Hindcast Skills on Weather Systems." Geosci. Model Dev. 19, 1937-1964. doi:10.5194/gmd-19-1937-2026.
  • Zhang, W, Z Hu, W Cai, GE Karniadakis. 2026. "Deep Neural networks for solving high-dimensional parabolic partial differential equations." Arxiv preprint arXiv:2601.13256v3.
  • Qin, H, Z Gao, J Shen, G Karniadakis. 2026. "Nonlinear parameterization solver for fractional Burger’s equations." Arxiv preprint arXiv:2601.04482v1.

2025

  • Yang CF, Y Cui, and DM Tartakovsky. 2025. "Microkinetic models of electrochemistry: Assumptions, limitations, and failures." Journal of Physical Chemistry C 128(38), 17080-17090. doi:10.1021/acs.jpcc.5c04319.
  • Chiofalo A, V Ciriello, and DM Tartakovsky. 2025. "Transfer learning for surrogate construction from multifidelity groundwater simulations." Advances in Water Resources 206, 105140. doi:10.1016/j.advwatres.2025.105140.
  • Liu C, T Roy, DM Tartakovsky, and D Dwivedi. 2025. "Baseflow identification via explainable AI with Kolmogorov-Arnold networks." Journal of Geophysical Research: Machine Learning and Computation 2(4), e2025JH000749. doi:10.1029/2025JH000749.
  • Yang X and DM Tartakovsky. 2025. "Electrochemical multilayer perceptron for lithium batteries." Journal of Power Sources 659, 238334. doi:10.1016/j.jpowsour.2025.238334.
  • Shelley A, A Olson, G Geraci, and DM Tartakovsky. 2025. "Multipoint correlations in Poisson media." Physical Review Letters 135. doi:10.1103/325k-g4dr.
  • Zhao H and DM Tartakovsky. 2025. "Discovery of linear representations for nonautonomous translation-invariant problems." SIAM Journal on Scientific Computing 47(4), A2074-A2097. doi:10.1137/24M1632905.
  • Chandra A, T Kapoor, B Daniels, M Curti, K Tiels, DM Tartakovsky, and EA Lomonova. 2025. "Generalizable models of magnetic hysteresis via physics-aware recurrent neural networks." Computer Physics Communications 314, 109650. doi:10.1016/j.cpc.2025.109650.
  • Abylkhani B, D Dwivedi, SB Yabusaki, and DM Tartakovsky. 2025. "Domain decomposition for enhancement of reduced-order models." Journal of Machine Learning for Modeling and Computing 6(3), 19-36. doi:10.1615/JMachLearnModelComput.2025059012.
  • Chiofalo A, L Careddu, V Ciriello, and DM Tartakovsky. 2025. "AI-enabled cardiovascular models trained on multifidelity simulations data." Journal of Machine Learning for Modeling and Computing 6(3), 1-17. doi:10.1615/JMachLearnModelComput.2025058368.
  • Propp AM and DM Tartakovsky. 2025. "Transfer learning on multi-dimensional data: a novel approach to neural network-based surrogate modeling." Journal of Machine Learning for Modeling and Computing 6(2), 13-27. doi:10.1615/JMachLearnModelComput.2024057138.
  • Queiruga AF, T Gutman-Solo, and S Jiang. 2025. "Interpretability and generalization bounds for learning spatial physics." arXiv preprint arXiv:2506.15199. (ICML 2026)
  • Jing C, UB Mudiyanselage, W Cho, M Jo, A Gruber, and K Lee. 2025. "Meta-learning structure-preserving dynamics." arXiv preprint arXiv:2508.11205. (ICML 2026)
  • He X, Y Shin, A Gruber, S Jung, K Lee, and Y Choi. 2025. "Thermodynamically consistent latent dynamics identification for parametric systems." Transactions on Machine Learning Research. arXiv preprint arXiv:2506.08475.
  • Cyr EC, J Hahne, NS Moore, JB Schroder, BS Southworth, and DA Vargas. 2025. "TorchBraid: High-performance layer-parallel training of deep neural networks with MPI and GPU acceleration." ACM Transactions on Mathematical Software 51(3), 1-30. doi:10.1145/3759244
  • Voronin A, G Harper, S MacLachlan, LN Olson, and RS Tuminaro. 2025. "Monolithic multigrid preconditioners for high-order discretizations of Stokes equations." SIAM Journal on Scientific Computing. doi:10.1137/24M1675588.
  • Propp AM, M Perego, EC Cyr, A Gruber, AA Howard, A Heinlein, P Stinis, and DM Tartakovsky. 2025. "Domain-decomposed graph neural network surrogate modeling for ice sheets." arXiv preprint arXiv:2512.01888.
  • Actor JA, A Gruber, and EC Cyr. 2025. "Deriving transformer architectures as implicit multinomial regression." arXiv preprint arXiv:2509.04653.
  • Lee D, A Sima, Y Li, P Stinis, and P Panda. 2025. "SpikePool: Event-driven spiking transformer with pooling attention." arXiv preprint arXiv:2510.12102.
  • Bhattacharjee A, A Moitra, R Yin, and P Panda. 2025. "SITRA: Exploiting temporal silence in spiking transformers for fast and energy-efficient inference." 2025 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED), 1-7. doi:10.1109/ISLPED65674.2025.11261761
  • Lee D, Y Li, R Yin, S Xiao, and P Panda. 2025. "Memba: Membrane-driven parameter-efficient fine-tuning for Mamba." arXiv preprint arXiv:2506.18184. (ICLR 2026)
  • Fang W and P Panda. 2025. "Event2Vec: Processing neuromorphic events directly by representations in vector space." arXiv preprint arXiv:2504.15371. (ICML 2026)
  • Xiao S, Y Li, Y Kim, D Lee, and P Panda. 2025. "Respike: Residual frames-based hybrid spiking neural networks for efficient action recognition." Neuromorphic Computing and Engineering 5(1), 014009. doi:10.1088/2634-4386/adb070
  • Lee D, Y Li, Y Kim, S Xiao, and P Panda. 2025. "Spiking transformer with spatial-temporal attention." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 13948-13958.
  • Shaffer B, B Kinch, J Klobusicky, MA Hsieh, and N Trask. 2025. "Physics-informed sensor coverage through structure preserving machine learning." arXiv preprint arXiv:2509.10363. (submitted to SIAM SISC)
  • Kinch B, B Shaffer, E Armstrong, M Meehan, J Hewson, and N Trask. 2025. "Structure-preserving digital twins via conditional neural Whitney forms." arXiv preprint arXiv:2508.06981. (submitted to Journal of Computational Physics)
  • Shaffer B, V Edwards, B Kinch, N Trask, and M Hsieh. 2025. "Multi-robot multi-source localization in complex flows with physics-preserving environment models." arXiv preprint arXiv:2509.14228. (ICRA)
  • Hernandez Q, M Win, T O'Connor, P Arratia, and N Trask. 2025. "Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systems." Proceedings of the National Academy of Sciences. doi:10.1073/pnas.2519631123.
  • Bourdais T and H Owhadi. 2025. "Minimal variance model aggregation: A principled, non-intrusive, and versatile integration of black box models." ICLR 2025.
  • Huan S, J Guinness, M Katzfuss, H Owhadi, and F Schäfer. 2025. "Sparse Cholesky factorization by greedy conditional selection." SIAM/ASA Journal on Uncertainty Quantification 13(3), 1649-1679. doi:10.1137/23M1606253
  • Lee J, B Hamzi, Y Kevrekidis, and H Owhadi. 2025. "Gaussian processes simplify differential equations." Physica D: Nonlinear Phenomena, 134988. doi:10.1016/j.physd.2025.134988
  • Chen Y, B Hosseini, H Owhadi, and AM Stuart. 2025. "Gaussian measures conditioned on nonlinear observations: Consistency, MAP estimators, and simulation." Statistics and Computing 35(1), 1-23. doi:10.1007/s11222-024-10535-0
  • Jalalian Y, JF Osorio Ramirez, A Hsu, B Hosseini, and H Owhadi. 2025. "Data-efficient kernel methods for learning differential equations and their solution operators: Algorithms and error analysis." arXiv preprint arXiv:2503.01036.
  • Nelsen NH, H Owhadi, AM Stuart, X Yang, and Z Zou. 2025. "Bilevel optimization for learning hyperparameters: Application to solving PDEs and inverse problems with Gaussian processes." arXiv preprint arXiv:2510.05568.
  • Batlle P, P Patil, M Stanley, J Ruiz Lupon, H Owhadi, and M Kuusela. 2025. "Simultaneous frequentist calibration of confidence regions for multiple functionals in constrained inverse problems." arXiv preprint arXiv:2510.11708.
  • Stanley M, P Batlle, P Patil, H Owhadi, and M Kuusela. 2025. "Confidence intervals for functionals in constrained inverse problems via data-adaptive sampling-based calibration." arXiv preprint arXiv:2502.02674.
  • Hu Z, Z Yang, Y Wang, GE Karniadakis, K Kawaguchi. 2025. “Bias-variance trade-off in physics-informed neural networks with randomized smoothing for high-dimensional PDEs.” SIAM Journal on Scientific Computing 47 (4), C846-C872. doi:10.1137/23M1621356
  • Zhuang Q., CZ Yao, Z Zhang, GE Karniadakis. 2025. “Two-Scale Neural Networks for Partial Differential Equations with Small Parameters”. Communications in Computational Physics 38 (3), 603-629. doi:10.4208/cicp.OA-2024-0040
  • Hu Z, Z Zhang, GE Karniadakis, K Kawaguchi. 2025. “Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker-Planck Equations.” SIAM Journal on Scientific Computing 47 (3), C680-C705. doi:10.1137/24M1638768
  • Lee, Y, F L Florencio, J Pathak, GE Karniadakis. 2025. "Hybrid Iterative Solvers with Geometry-Aware Neural Preconditioners for Parametric PDEs." Arxiv preprint arXiv:2512.14596.
  • Shiratori, S, E Kiyani, K Shukla, GE Karniadakis. 2025. "GIMLET: Generalizable and Interpretable Model Learning through Embedded Thermodynamics." Arxiv preprint arXiv:2512.19936.
  • Chen, Y, H Owhadi, and F Schäfer. 2025. "Sparse Cholesky factorization for solving nonlinear PDEs via Gaussian processes," Math. Comp. 94 no. 353, 1235–1280. doi:10.1090/S0025-5718-2024-03992-3.
  • Jalalian, Y, M Samir, B Hamzi, P Tavallali, H Owhadi. 2025. "Data-efficient Kernel Methods for Learning Hamiltonian Systems." arXiv preprint arXiv:2509.17154
  • Bacho, A, A G Sorokin, X Yang, T Bourdais, E Calvello, M Darcy, A Hsu, B Hosseini, H Owhadi. 2025 "Operator Learning at Machine Precision." arXiv preprint arXiv:2511.19980
  • Zou, Z, Z Wang, and G Karniadakis, 2025. "Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 481(2325). doi:10.1098/rspa.2025.0205
  • Wen S, A Khumbhat, L Lingsch, S Mousavi, Y Zhao, P Chandrashekhar, S Mishra. 2025. "Geometry Aware Operator Transformer as an Efficient and Accurate Neural Surrogate for PDEs on Arbitrary Domains." arXiv preprint arXiv:2505.18781v3.
  • Shende S, V Narayanan, V Fenn, Y Huang, D Goksuluk, G Choudhary, M Agraz, M Xu. 2025. "RGE-GCN: Recursive Gene Elimination with Graph Convolutional Networks for RNA-seq based Early Cancer Detection." arXiv preprint arXiv:2512.04333v1. 
  • Toscano JD, DT Chen, GE Karniadakis. 2025. "ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms." arXiv preprint arXiv:2512.03476v1.  
  • Bora A, S Zhang, K Shukla, BE Harrop, G Karniadakis, LR Leung. 2025. "Retrofitting Earth System Models with Cadence-Limited Neural Operator Updates." arXiv preprint arXiv:2512.03309v1. 
  • Wan R, GE Karniadakis, P Stinis. 2025. "From LIF to QIF: Toward Differentiable Spiking Neurons for Scientific Machine Learning." arXiv preprint arXiv:2511.06614v1. 
  • Kumar V, GE Karniadakis. 2025. "Toward Autonomous Engineering Design: A Knowledge-Guided Multi-Agent Framework." arXiv preprint arXiv:2511.03179v2. 
  • Patil S, Z Zhang, Y Huang, T Ma, M Xu. 2025. "Hyperbolic Large Language Models." arXiv preprint arXiv:2509.05757.
  • Gao Z, GE Karniadakis. 2025. "Scalable Bayesian physics-informed Kolmogorov-Arnold networks." SIAM/ASA Journal on Uncertainty Quantification. 30;13(3):1543-77. doi:10.1137/25M1729253
  • Gao Z, G Karniadakis. 2025. "Safe cross-entropy-based importance sampling for rare event simulations." arXiv preprint arXiv:2509.07160. 
  • Kumar V, J Bi, CN Ngoc, V Oancea, GE Karniadakis. 2025. "Learning Nonlinear Responses in PET Bottle Buckling with a Hybrid DeepONet-Transolver Framework." arXiv preprint arXiv:2509.13520. 
  • Kiyani E, K Shukla, JF Urbán, J Darbon, GE Karniadakis. 2025. "Optimizing the optimizer for physics-informed neural networks and Kolmogorov-Arnold networks." Computer Methods in Applied Mechanics and Engineering 446:118308. doi:10.1016/j.cma.2025.118308
  • Huang Y, A Nouranizadeh, C Ahrends, M Xu. 2025. "BrainATCL: Adaptive Temporal Brain Connectivity Learning for Functional Link Prediction and Age Estimation." arXiv preprint arXiv:2508.07106. 
  • Lee Y, S Liu, Z Zou, A Kahana, E Turkel, R Ranade, J Pathak, GE Karniadakis. 2025. "Fast meta-solvers for 3D complex-shape scatterers using neural operators trained on a non-scattering problem." Computer Methods in Applied Mechanics and Engineering 446:118231. doi:10.1016/j.cma.2025.118231 
  • Kopanicakova A, Y Lee, GE Karniadakis.  2025. “Leveraging Operator Learning to Accelerate Convergence of the Preconditioned Conjugate Gradient method”. Machine Learning for Computational Science and Engineering 1, 39. doi:10.1007/s44379-025-00039-7
  • Batlle P, Y Chen, B Hosseini, H Owhadi, and AM Stuart. 2025. “Error Analysis of Kernel/GP Methods for Nonlinear and Parametric PDEs.” Journal of Computational Physics, 520, 113488, doi:10.1016/j.jcp.2024.113488.
  • Candelori L, AG Abanov, J Berger, CJ Hogan, V Kirakosyan, K Musaelian, R Samson, JE Smith, D Villani, MT Wells, and M Xu. 2025. “Robust estimation of the intrinsic dimension of data sets with quantum cognition machine learning.” Scientific Reports 15(1):6933. doi:10.1038/s41598-025-91676-8.
  • Chen W, AA Howard, and P Stinis. 2025. "Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks." Journal of Computational Physics, 114226. doi:10.1016/j.jcp.2025.114226.
  • Howard A, B Jacob, and P Stinis. 2025. “Multifidelity Kolmogorov-Arnold Networks.” Machine Learning: Science and Technology 6, 035038. PNNL-SA-210528. doi:10.1088/2632-2153/adf702.
  • Kopaničáková A, GE Karniadakis. 2025. “DeepONet based preconditioning strategies for solving parametric linear systems of equations”. SIAM Journal on Scientific Computing 47(1):C151–81. doi:10.1137/24M162861X.
  • Kumar V, S Goswami, K Kontolati, MD Shields, GE Karniadakis. 2025. “Synergistic learning with multi-task DeepONet for efficient PDE problem solving”. Neural Networks 184:107113. doi:10.1016/j.neunet.2024.107113.
  • Mousavi S, S Wen, L Lingsch, M Herde, B Bogdan Raonic, S Mishra. 2025. “RIGNO: A Graph-based framework for robust and accurate operator learning for PDEs on arbitrary domains”. arXiv preprint arXiv:2501.19205.
  • Oommen V, A Bora, Z Zhang, GE Karniadakis. 2025. “Integrating neural operators with diffusion models improves spectral representation in turbulence modelling”. Proc. R. Soc. A. 481: 20240819. doi:10.1098/rspa.2024.0819.
  • Ovadia O, V Oommen, A Kahana, A Peyvan, E Turkel, GE Karniadakis. 2025. “Real-time inference and extrapolation with Time-Conditioned UNet: Applications in hypersonic flows, incompressible flows, and global temperature forecasting”. Computer Methods in Applied Mechanics and Engineering 441:117982. doi:10.1016/j.cma.2025.117982.
  • Patil S, AP Pandey, I Koutis, M Xu. 2025. “Hierarchical Mamba Meets Hyperbolic Geometry: A New Paradigm for Structured Language Embeddings”. arXiv preprint arXiv:2505.18973.
  • Ramirez H, D Tabarelli, A Brancaccio, P Belardinelli, EB Marsh, M Funke, JC Mosher, F Maestu, M Xu, D Pantazis. 2025. “Fully hyperbolic neural networks: A novel approach to studying aging trajectories”. IEEE Journal of Biomedical and Health Informatics 29(6):4463–4473. doi:10.1109/jbhi.2025.3540937.
  • Sanderse B, P Stinis, S Ahmed, and R Maulik. 2025. “Scientific machine learning for closure models in multiscale problems: A review.” Foundations of Data Science 7(1): 298-337. doi:10.3934/fods.2024043.
  • Shukla K, Z Zou, CH Chan, A Pandey, Z Wang, GE Karniadakis. 2025. “NeuroSEM: A hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements.” Computer Methods in Applied Mechanics and Engineering 433, part A, 1177498. doi:10.1016/j.cma.2024.117498.
  • Toscano JD, T Käufer, Z Wang, M Maxey, C Cierpka, GE Karniadakis. 2025. “AIVT: Inference of turbulent thermal convection from measured 3D velocity data by physics-informed Kolmogorov-Arnold networks”. Science Advances 11(19): eads5236. doi:10.1126/sciadv.ads5236.
  • Toscano JD, V Oommen, AJ Varghese, Z Zou, N Ahmadi Daryakenari, C Wu, GE Karniadakis. 2025. “From PINNS to PIKANS: Recent advances in physics-informed machine learning”. Machine Learning for Computational Science and Engineering 1(1):1–43. doi:10.1007/s44379-025-00015-1.
  • Varghese AJ, Z Zhang, GE Karniadakis. 2025. “SympGNNs: Symplectic Graph Neural Networks for identifying high-dimensional Hamiltonian systems and node classification”. Neural Networks 187:107397. doi:10.1016/j.neunet.2025.107397.
  • Wang T, Z Hu, K Kawaguchi, Z Zhang, GE Karniadakis. 2025. “Tensor neural networks for high-dimensional Fokker-Planck equations”. Neural Networks 185:107165.  doi:10.1016/j.neunet.2025.107165.
  • Zhang Q, C Wu, A Kahana, GE Karniadakis, Y Kim, Y Li, P Panda. 2025. “Artificial to Spiking Neural Networks Conversion with Calibration in Scientific Machine Learning”. SIAM Journal on Scientific Computing 47(3):C559–77. doi:10.1137/24M1643232.
  • Zou Z, GE Karniadakis. 2025. “Multi-head physics-informed neural networks for learning functional priors and uncertainty quantification”. Journal of Computational Physics 531:113947. doi:10.1016/j.jcp.2025.113947.

2024

  • Libero G, V Ciriello, and DM Tartakovsky. 2024. "Dynamic mode decomposition of GRACE satellite data." Advances in Water Resources 193, 104834. doi:10.1016/j.advwatres.2024.104834.
  • Monteiro HBN and DM Tartakovsky. 2024. "A meshless stochastic method for Poisson-Nernst-Planck equations." Journal of Chemical Physics 161(5), 054106. doi:10.1063/5.0223018.
  • Libero G, A Chiofalo, V Ciriello, and DM Tartakovsky. 2024. "Extended dynamic mode decomposition for model reduction in fluid dynamics simulations." Physics of Fluids 36, 061911. doi:10.1063/5.0207957.
  • Chandra A, J Bakarji, and DM Tartakovsky. 2024. "Role of physics in physics-informed machine learning." Journal of Machine Learning for Modeling and Computing 5(1), 85-97. doi:10.1615/JMachLearnModelComput.2024053170.
  • Lu H and DM Tartakovsky. 2024. "Data-driven models of nonautonomous systems." Journal of Computational Physics 507, 112976. doi:10.1016/j.jcp.2024.112976.
  • Libero G, DM Tartakovsky, and V Ciriello. 2024. "Polynomial chaos enhanced by dynamic mode decomposition for order-reduction of dynamic models." Advances in Water Resources 186, 104677. doi:10.1016/j.advwatres.2024.104677.
  • Kemkar S, M Tao, A Ghosh, G Stamatakos, N Graf, K Poorey, U Balakrishnan, N Trask, and R Radhakrishnan. 2024. "Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine." Frontiers in Physiology 15, 1473125. doi:10.3389/fphys.2024.1473125
  • Walker E, JA Actor, C Martinez, and N Trask. 2024. "Flow-based parameterization for DAG and feature discovery in scientific multimodal data." Frontiers in Mechanical Engineering 10, 1408649. doi:10.3389/fmech.2024.1408649
  • Actor JA, X Hu, A Huang, SA Roberts, and N Trask. 2024. “Data-driven Whitney forms for structure-preserving control volume analysis.” Journal of Computational Physics 496 (2024): 112520. doi:10.1016/j.jcp.2023.112520.
  • Actor JA, A Gruber, EC Cyr, and N Trask. 2024. "Gaussian Variational Schemes on Bounded and Unbounded Domains." arXiv preprint arXiv:2410.06219. 
  • Anagnostopoulos SJ, JD Toscano, N Stergiopulos, and GE Karniadakis. 2024. “Learning in PINNs: Phase transition, total diffusion, and generalization.” arXiv preprint arXiv:2403.18494.
  • Anagnostopoulos SJ, JD Toscano, N Stergiopulos, GE Karniadakis. 2024. “Residual-based attention in physics-informed neural networks”. Computer Methods in Applied Mechanics and Engineering 421:116805. doi:10.1016/j.cma.2024.116805.
  • Armstrong E, MA Hansen, RC Knaus, NA Trask, JC Hewson, JC Sutherland. 2024. "Accurate compression of tabulated chemistry models with partition of unity networks." Combustion Science and Technology 196, 850–867. doi:10.1080/00102202.2022.2102908.
  • Baker C, I Suárez-Méndez, G Smith, EB Marsh, M Funke, JC Mosher, F Maestú, M Xu, and D Pantazis. 2024. “Hyperbolic graph embedding of MEG brain networks to study brain alterations in individuals with subjective cognitive decline.” IEEE Journal of Biomedical and Health Informatics, pp 1-17. doi:10.1109/JBHI.2024.3416890.
  • Batlle P, M Darcy, B Hosseini, and H Owhadi. 2024. “Kernel Methods are Competitive for Operator Learning.” Journal of Computational Physics, Volume 496, 2024,112549. doi:10.1016/j.jcp.2023.112549.
  • Bhattacharjee A, R Yin, A Moitra, and P Panda. 2024. “Are SNNs Truly Energy-efficient?—A Hardware Perspective,” ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 13311–13315. doi: 10.1109/ICASSP48485.2024.10448269.
  • Bourdais T, P Batlle, X Yang, R Baptista, N Rouquette, and H Owhadi. 2024. “Computational Hypergraph Discovery, a Gaussian Process framework for connecting the dots.” Proceedings of the National Academy of Sciences (PNAS) 121 (32) e2403449121. doi:10.1073/pnas.2403449121
  • Bourdais T and H Owhadi. 2024. “Model aggregation: minimizing empirical variance outperforms minimizing empirical error.” arXiv preprint arXiv:2409.17267.
  • Bourdais T, et al. 2024. “Codiscovering graphical structure and functional relationships within data: A Gaussian Process framework for connecting the dots.” Proceedings of the National Academy of Sciences 121, e2403449121. doi:10.1073/pnas.2403449121
  • Cao Q, S Goswami, and GE Karniadakis. 2024. "Laplace neural operator for solving differential equations. Nature Machine Intelligence 6, 631–640. doi:10.1038/s42256-024-00844-4
  • Cao Q, S Goswami, T Tripura, S Chakraborty, and GE Karniadakis. 2024. “Deep neural operators can predict the real-time response of floating offshore structures under irregular waves.” Computers & Structures 291: 107228. doi:10.1016/j.compstruc.2023.107228.
  • Chen P, J Darbon, and T Meng. 2024. “Hopf-Type Representation Formulas and Efficient Algorithms for Certain High-Dimensional Optimal Control Problems.” Computers & Mathematics with Applications 161: 90-120. doi:10.1016/j.camwa.2024.02.037.
  • Chen P, J Darbon, and T Meng. 2024. “Lax-Oleinik-Type Formulas and Efficient Algorithms for Certain High-Dimensional Optimal Control Problems.” Communications on Applied Mathematics and Computation 04/29/2024. doi:10.1007/s42967-024-00371-4.
  • Chen W, P Gao, and P Stinis. 2024. “Physics-informed machine learning of the correlation functions in bulk fluids.” Physics of Fluids 36. doi:10.1063/5.0175065.
  • Chen P, T Meng, Z Zou, J Darbon and GE Karniadakis. 2024. “Leveraging Multi-time Hamilton-Jacobi PDEs for Certain Scientific Machine Learning Problems.” SIAM Journal on Scientific Computing 46 (2): C216--C248. doi:10.1137/23M1561397.
  • Chen P, T Meng, Z Zou, J Darbon, and GE Karniadakis. 2024. “Leveraging Hamilton-Jacobi PDEs with time-dependent Hamiltonians for continual scientific machine learning,” Proceedings of the 6th Annual Learning for Dynamics & Control Conference, 242:1–12. Available from https://proceedings.mlr.press/v242/chen24a.html.
  • Chen W and P Stinis. 2024. “Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations.” Journal of Computational Physics 498,112683. doi:10.1016/j.jcp.2023.112683.
  • Choi J, H Wi, J Kim, Y Shin, K Lee, N Trask, N Park. 2024. “Graph Convolutions Enrich the Self- Attention in Transformers!” arXiv preprint arXiv:2312.04234. 
  • Cyr EC. 2024. “A 2-Level Domain Decomposition Preconditioner for KKT Systems with Heat-Equation Constraints.” In: Dostál, Z., et al. Domain Decomposition Methods in Science and Engineering XXVII. DD 2022. Lecture Notes in Computational Science and Engineering, vol 149. Springer, Cham. doi:10.1007/978-3-031-50769-4_55.
  • Galanti T, M Xu, L Galanti, T Poggio. 2024. “Norm-based Generalization Bounds for Compositionally Sparse Neural Networks,” Advances in Neural Information Processing Systems 36. arXiv preprint arXiv:2301.12033v1.
  • Gao P, GE Karniadakis, and P Stinis. 2024. “Multiscale modeling framework of a constrained fluid with complex boundaries using twin neural networks.” arXiv preprint arXiv:2408.03263.
  • Gruber A, K Lee, H Lim, N Park, N Trask. 2024. “Efficiently Parameterized Neural Metriplectic Systems.” arXiv preprint arXiv:2405.16305.
  • Gruber A, K Lee, and N Trask. 2024. “Reversible and irreversible bracket-based dynamics for deep graph neural networks,” Advances in Neural Information Processing Systems, 1670, 38454 – 38484. doi: 10.5555/3666122.3667792.
  • Heinlein A, AA Howard, D Beecroft, and P Stinis. 2024. “Multifidelity domain decomposition-based physics-informed neural networks for time-dependent problems.” arXiv preprint arXiv:2401.07888.
  • Howard AA, B Jacob, SH Murphy, A Heinlein, and P Stinis. 2024. “Finite basis Kolmogorov-Arnold networks: domain decomposition for data-driven and physics-informed problems.” arXiv preprint arXiv:2406.19662 
  • Howard AA, SH Murphy, SE Ahmed, P Stinis. 2024. “Stacked networks improve physics-informed training: Applications to neural networks and deep operator networks.” Foundations of Data Science. doi:10.3934/fods.2024029
  • Howard AA, S Qadeer, AW Engel, A Tsou, M Vargas, T Chiang, P Stinis. 2024. “The conjugate kernel for efficient training of physics-informed deep operator networks,” ICLR 2024 Workshop on AI4DifferentialEquations In Science.
  • Hu Z, K Kawaguchi, Z Zhang, GE Karniadakis. 2024. “Tackling the curse of dimensionality in fractional and tempered fractional PDEs with physics-informed neural networks”. Computer Methods in Applied Mechanics and Engineering 432:117448. doi:10.1016/j.cma.2024.117448.
  • Hu Z, Z Shi, GE Karniadakis, and K Kawaguchi. 2024. “Hutchinson trace estimation for high-dimensional and high-order physics-informed neural networks.” Computer Methods in Applied Mechanics and Engineering 424: 116883. doi:10.1016/j.cma.2024.116883.
  • Hu Z, K Shukla, GE Karniadakis, and K Kawaguchi. 2024. “Tackling the curse of dimensionality with physics-informed neural networks.” Neural Networks 176: 106369. doi:10.1016/j.neunet.2024.106369.
  • Jiang S, J Actor, S Roberts, and N Trask. 2024. “Chapter 10 - A structure-preserving domain decomposition method for data-driven modeling.” Handbook of Numerical Analysis, vol 25, 469-514, doi:10.1016/bs.hna.2024.05.011.
  • Kim Y, A Kahana, R Yin, Y Li, P Stinis, GE Karniadakis, and P Panda. 2024. “Rethinking skip connections in Spiking Neural Networks with Time-To-First-Spike coding.” Frontiers in Neuroscience 18: 1346805. doi:10.3389/fnins.2024.1346805.
  • Kuberry P, P Bochev, J Koester, et al. 2024. “A discontinuous piecewise polynomial generalized moving least squares scheme for robust finite element analysis on arbitrary grids.” Engineering with Computers. doi:10.1007/s00366-024-02036-5.
  • Langlois GP, J Buch, and J Darbon. 2024. “Efficient first-order algorithms for large-scale, non-smooth maximum entropy models with application to wildfire science.” arXiv preprint arXiv:2403.06816.
  • Lee D, R Yin, Y Kim, A Moitra, Y Li, and P Panda. 2024. “TT-SNN: Tensor Train Decomposition for Efficient Spiking Neural Network Training,” 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE), Valencia, Spain, 2024, pp. 1-6, doi:10.23919/DATE58400.2024.10546679.
  • Li Y, T Geller, Y Kim, and P Panda. 2024. “Seenn: Towards temporal spiking early exit neural networks,” Advances in Neural Information Processing Systems 36. arXiv preprint arXiv:2304.01230.
  • Michałowska K, S Goswami, GE Karniadakis, and S Riemer-Sørensen. 2024. “Neural Operator Learning for Long-Time Integration in Dynamical Systems with Recurrent Neural Networks.” 2024 International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan, 2024, pp. 1–8. doi:10.1109/IJCNN60899.2024.10650331.
  • Meng T, Z Zou, J Darbon, and GE Karniadakis. 2024. “HJ-sampler: A Bayesian sampler for inverse problems of a stochastic process by leveraging Hamilton-Jacobi PDEs and score-based generative models.” arXiv preprint arXiv:2409.09614. 
  • Moore NS, EC Cyr, P Ohm, CM Siefert, RS Tuminaro. 2024. “Graph neural networks and applied linear algebra.” arXiv preprint arXiv:2310.14084.
  • Ovadia O, A Kahana, P Stinis, E Turkel, D Givoli, and GE Karniadakis. 2024. “Vito: Vision Transformer-Operator.” Computer Methods in Applied Mechanics and Engineering 428, 117109. doi:10.1016/j.cma.2024.117109.
  • Pandey AP, AJ Varghese, S Patil, M Xu. 2024. “A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers”. arXiv preprint arXiv:2412.11293.
  • Qiang Y, M Xu, MP Pochron, M Jupelli, and M Dao. 2024. “A framework of computer vision- enhanced microfluidic approach for automated assessment of the transient sickling kinetics in sickle red blood cells.” Frontiers in Physics 12: 1331047. doi:10.3389/fphy.2024.1331047. 
  • Schäfer F, H Owhadi. 2024. “Sparse recovery of elliptic solvers from matrix-vector products.” SIAM Journal on Scientific Computing 46 (2), A998-A1025. doi:10.1137/22M154226X
  • Sentz P, K Beckwith, EC  Cyr, LN Olson, R Patel. 2024. “Reduced Basis Approximations of Parameterized Dynamical Partial Differential Equations via Neural Networks.” Foundations of Data Science. doi:10.3934/fods.2024044.
  • Shukla K, JD Toscano, Z Wang, Z Zou, GE Karniadakis. 2024. “A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks”. Computer Methods in Applied Mechanics and Engineering 431:117290. doi:10.1016/j.cma.2024.117290.
  • Theilman BH, Q Zhang, A Kahana, EC Cyr, N Trask, JB Aimone, and GE Karniadakis. 2024. “Spiking Physics-Informed Neural Networks on Loihi 2,” in 2024 Neuro Inspired Computational Elements Conference (NICE), La Jolla, CA, pp. 1-6, doi:10.1109/NICE61972.2024.10548180.
  • Toscano JD, T Käufer, Z Wang, M Maxey, C Cierpka, and GE Karniadakis. 2024. “Inferring turbulent velocity and temperature fields and their statistics from Lagrangian velocity measurements using physics-informed Kolmogorov-Arnold Networks.” arXiv preprint arXiv:2407.15727.
  • Toscano JD, L-L Wang, and GE Karniadakis. 2024. "KKANs: Kurkova-Kolmogorov-Arnold Networks and Their Learning Dynamics." arXiv preprint arXiv:2412.16738.
  • Toscano JD, C Wu, A Ladrón-de-Guevara, T Du, M Nedergaard, DH Kelley, GE Karniadakis, and KAS Boster. 2024. "Inferring in vivo murine cerebrospinal fluid flow using artificial intelligence velocimetry with moving boundaries and uncertainty quantification." Interface Focus 14, 6, 20240030. doi:10.1098/rsfs.2024.0030
  • Trask N, C Martinez, T Shilt, E Walker, K Lee, A Garland, DP Adams, JF Curry, MT Dugger, SR Larson, et al. 2024. “Unsupervised physics-informed disentanglement of multimodal materials data.” Materials Today. doi:10.1016/j.mattod.2024.09.005.
  • Varghese AJ, A Bora, M Xu, and GE Karniadakis. 2024. “TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers.” Neural Networks 172, 106086. doi:10.1016/j.neunet.2023.12.040.
  • Verburg C, A Heinlein, EC Cyr. 2024. “DDU-Net: A Domain Decomposition-based CNN for High-Resolution Image Segmentation on Multiple GPUs.” arXiv preprint arXiv:2407.21266.
  • Walker E, N Trask, C Martinez, K Lee, JA Actor, S Saha, T Shilt, D Vizoso, R Dingreville, BL Boyce. 2024. "Unsupervised physics-informed disentanglement of multimodal data." Foundations of Data Science. doi:10.3934/fods.2024019
  • Wan R, Q Zhang, GE Karniadakis. 2024. “Randomized Forward Mode Gradient for Spiking Neural Networks in Scientific Machine Learning”. arXiv preprint arXiv:2411.07057.
  • Williams E, A Howard, B Meris, and P Stinis. 2024. “What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications.” arXiv preprint arXiv:2411.18459. 
  • Wu X, N Trask, and J Chan. 2024. “Entropy stable discontinuous Galerkin methods for the shallow water equations with subcell positivity preservation.” Numerical Methods for Partial Differential Equations, 40,6. doi:10.1002/num.23129.
  • Yang L, X Sun, B Hamzi, H Owhadi, and N Xie. 2024. “Learning Dynamical Systems from Data: A Simple Cross-Validation Perspective, Part V: Sparse Kernel Flows for 132 Chaotic Dynamical Systems.” Physica D: Nonlinear Phenomena 460, 134070. doi:10.1016/j.physd.2024.134070
  • Yin R, Y Kim, Y Li, A Moitra, N Satpute, A Hambitzer, and P Panda. 2024. “Workload-balanced pruning for sparse spiking neural networks.” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 8, no. 4, pp. 2897-2907. doi:10.1109/TETCI.2024.3393367.
  • Yin R, Y Li, A Moitra, P Panda. 2024. “MINT: Multiplier-less INTeger Quantization for Energy Efficient Spiking Neural Networks,” 2024 29th Asia and South Pacific Design Automation Conference (ASP-DAC), Incheon, Korea, Republic of, pp. 830-835, doi:10.1109/ASP-DAC58780.2024.10473825.
  • Zhang Q, A Kahana, GE Karniadakis, and P Stinis. 2024. “SMS: Spiking marching scheme for efficient long time integration of differential equations.” Journal of Computational Physics 516, 113363. doi:10.1016/j.jcp.2024.113363.
  • Zhang E, A Kahana, A Kopaničáková, et al. 2024. “Blending neural operators and relaxation methods in PDE numerical solvers.” Nat Mach Intell (2024). doi:10.1038/s42256-024-00910-x.
  • Zhang E, A Kahana, E Turkel, R Ranade, J Pathak, and GE Karniadakis. 2024. “A hybrid iterative numerical transferable solver (HINTS) for PDEs based on deep operator network and relaxation methods,” arXiv preprint arXiv:2208.13273.
  • Zhang Z, Z Zou, E Kuhl, and GE Karniadakis. 2024. “Discovering a Reaction-Diffusion Model for Alzheimer's Disease by Combining PINNs with Symbolic Regression.” Computer Methods in Applied Mechanics and Engineering 419, 116647. doi:10.1016/j.cma.2023.116647.
  • Zou Z, T Meng, P Chen, J Darbon, and GE Karniadakis. 2024. “Leveraging viscous Hamilton-Jacobi PDEs for uncertainty quantification in scientific machine learning.” SIAM/ASA Journal on Uncertainty Quantification 12, 1165–1191. doi:10.1137/24M1646455.

  • Zou Z, X Meng, and G.E. Karniadakis. 2024. “Correcting model misspecification in physics-informed neural networks (PINNs).” Journal of Computational Physics 505, 112918. doi:10.1016/j.jcp.2024.112918.

    2023

  • Lu H and DM Tartakovsky. 2023. "DRIPS: A framework for dimension reduction and interpolation in parameter space." Journal of Computational Physics 493, 112455. doi:10.1016/j.jcp.2023.112455.
  • Lu H, F Giannino, and DM Tartakovsky. 2023. "Parsimonious models of in-host viral dynamics and immune response." Applied Mathematics Letters 145, 108781. doi:10.1016/j.aml.2023.108781.
  • Antolik JT, A Howard, F Vereda, N Ionkin, M Maxey, and DM Harris. 2023. “Hydrodynamic irreversibility of non-Brownian suspensions in highly confined duct flow." Journal of Fluid Mechanics 974:A11. doi:10.1017/jfm.2023.793.
  • Batlle P, P Patil, M Stanley, H Owhadi, and M Kuusela. 2023. “Optimization-based frequentist confidence intervals for functionals in constrained inverse problems: Resolving the Burrus conjecture.” arXiv preprint arXiv:2310.02461.
  • Bhattacharjee A, A Moitra, Y Kim, Y Venkatesha, and P Panda. 2023. “Examining the role and limits of batchnorm optimization to mitigate diverse hardware-noise in in-memory computing,” in Proceedings of the Great Lakes Symposium on VLSI 2023, 619–624. doi:10.1145/3583781.3590241.
  • Bhattacharjee A, A Moitra, and P Panda. 2023. "HyDe: A Hybrid PCM/FeFET/SRAM Device-search for Optimizing Area and Energy-efficiencies in Analog IMC Platforms." IEEE Journal on Emerging and Selected Topics in Circuits and Systems. doi:10.1109/JETCAS.2023.3327748.
  • Bhattacharjee A, A Moitra, and P Panda. 2023. "XploreNAS: Explore Adversarially Robust & Hardware-efficient Neural Architectures for Non-ideal Xbars." ACM Transactions on Embedded Computing Systems, vol. 22, no. 4, pp 1-17. doi:10.1145/3593045.
  • Bourdais T, P Batlle, X Yang, R Baptista, N Rouquette, and H Owhadi. 2023. “Computational Hypergraph Discovery, a Gaussian Process framework for connecting the dots.” arXiv preprint arXiv:2311.17007.
  • Chen P, T Meng, Z Zou, J Darbon and GE Karniadakis. 2023. “Leveraging Hamilton-Jacobi PDEs with Time-Dependent Hamiltonians for Continual Scientific Machine Learning.” arXiv preprint arXiv:2311.07790.
  • Cyr EC. 2023. “A 2-Level Domain Decomposition Preconditioner for KKT Systems with Heat-Equation Constraints.” arXiv preprint arXiv:2305.04421.
  • De Florio M, A Kahana, and GE Karniadakis. 2023. “Analysis of biologically plausible neuron models for regression with spiking neural networks.” arXiv preprint arXiv:2401.00369.
  • Galanti T, M Xu, L Galanti and T Poggio. 2023. “Norm-based Generalization Bounds for Compositionally Sparse Neural Networks.” arXiv preprint arXiv:2301.12033.
  • Harlev A, A Engel, P Stinis, and T Chiang. 2023. "Exploring Learned Representations of Neural Networks with Principal Component Analysis." arXiv preprint arXiv:2309.15328.
  • He Q, M Perego, A Howard, G Karniadakis, and P Stinis. 2023. “A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling.” Journal of Computational Physics 492, 112428. doi:10.1016/j.jcp.2023.112428.
  • Howard AA, J Dong, R Patel, M D’Elia, MR Maxey and P Stinis. 2023. “Machine Learning Methods for Particle Stress Development in Suspension Poiseuille Flows.” Rheologica Acta 62, 507-534. doi:10.1007/s00397-023-01413-z
  • Howard AA, M Perego, GE Karniadakis, and S Panos. 2023. "Multifidelity deep operator networks for data-driven and physics-informed problems." Journal of Computational Physics 493, 112462. doi:10.1016/j.jcp.2023.112462.
  • Kim Y, Y Li, A Moitra, R Yin, and P Panda. 2023. "Sharing Leaky-Integrate-and-Fire Neurons for Memory-Efficient Spiking Neural Networks." Frontiers in Neuroscience, vol. 37. doi:10.3389/fnins.2023.1230002.
  • Kim Y, Y Li, H Park, Y Venkatesha, A Hambitzer, and P Panda. 2023. “Exploring Temporal Information Dynamics in Spiking Neural Networks." arXiv preprint arXiv:2211.14406.
  • Kumar V, L Gleyzer, A Kahana, K Shukla and GE Karniadakis. 2023. “MYCRUNCHGPT: A LLM Assisted Framework for Scientific Machine Learning.” Journal of Machine Learning for Modeling and Computing 4(4), 41-72. doi:10.1615/JMachLearnModelComput.2023049518.
  • Lam R, A Sanchez-Gonzalez, M Willson, P Wirnsberger, M Fortunato, F Alet, S Ravuri, T Ewalds, Z Eaton-Rosen, W Hu, et al. 2023. “GraphCast: Learning skillful medium-range global weather forecasting.” Science 382,1416-1421. doi:10.1126/science.adi2336.
  • Li Y, T Geller, Y Kim, and P Panda.2023. “ SEENN: Towards Temporal Spiking Early-Exit Neural Networks." arXiv preprint arXiv:2304.01230.
  • Li Y, Y Kim, H Park, and P Panda. 2023. “Uncovering the Representation of Spiking Neural Networks Trained with Surrogate Gradient." arXiv preprint arXiv:2304.13098.
  • Li Y, A Moitra, T Geller, and P Panda. 2023. "Input-Aware Dynamic Timestep Spiking Neural Networks for Efficient In-Memory Computing." 60th ACM/IEEE Design Automation Conference (DAC), San Francisco, CA, pp. 1-6, doi:10.1109/DAC56929.2023.10247869.
  • Li Y, R Yin, Y Kim, and P Panda. 2023. "Efficient human activity recognition with spatio-temporal spiking neural networks." Frontiers in Neuroscience 17. doi:10.3389/fnins.2023.1233037.
  • Michałowska K, S Goswami, GE Karniadakis and S Riemer-Sørensen. 2023. “DON-LSTM: Multi-Resolution Learning with DeepONets and Long Short-Term Memory Neural Networks.” arXiv preprint arXiv:2310.02491.
  • Moitra A, A Bhattacharjee, Y Kim, and P Panda. 2023. “XPert: Peripheral Circuit & Neural Architecture Co-search for Area and Energy-efficient Xbar-based Computing." arXiv preprint arXiv:2303.17646.
  • Moitra A, A Bhattacharjee, R Kuang, G Krishnan, Y Cao, and P Panda. 2023. "Spikesim: An end-to-end compute-in-memory hardware evaluation tool for benchmarking spiking neural networks." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 42, no. 11, pp. 3815-3828. doi:10.1109/TCAD.2023.3274918.
  • Moitra A, R Yin, and P Panda. 2023. “Energy-efficient Hardware Design for Spiking Neural Networks,” 2023 57th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, pp. 543-544, doi:10.1109/IEEECONF59524.2023.10477043.
  • Moitra A, R Yin, and P Panda. 2023. “Hardware Accelerators for Spiking Neural Networks for Energy-Efficient Edge Computing,” in Proceedings of the Great Lakes Symposium on VLSI 2023, 137–138. doi:10.1145/3583781.3590254.
  • Moore NS, EC Cyr, P Ohm, CM Siefert, and RS Tuminaro. 2023. "Graph Neural Networks and Applied Linear Algebra." arXiv preprint arXiv:2310.14084. 
  • Nguyen D, A Bhattacharjee, A Moitra, and P Panda. 2023. “DeepCAM: A Fully CAM-based Inference Accelerator with Variable Hash Lengths for Energy-efficient Deep Neural Networks." arXiv preprint arXiv:2302.04712.
  • Ovadia O, E Turkel, A Kahana and GE Karniadakis. 2023. “DiTTO: Diffusion-inspired Temporal Transformer Operator.” arXiv preprint arXiv:2307.09072.
  • Owhadi H. 2023. “Gaussian process hydrodynamics." Applied Mathematics and Mechanic.-Engl. Ed. 44, 1175–1198. doi:10.1007/s10483-023-2990-9.
  • Qadeer S, A Engel, A Howard, A Tsou, M Vargas, P Stinis, and T Chiang. 2023. "Efficient kernel surrogates for neural network-based regression." arXiv preprint arXiv:2310.18612.
  • Shekarpaz S, F Zeng and GE Karniadakis. 2023. “Splitting Physics-Informed Neural Networks for Inferring the Dynamics of Integer and Fractional-Order Neuron Models.” arXiv preprint arXiv:2304.13205.
  • Theilman BH, F Wang, F Rothganger, and JB Aimone. 2023. "Decomposing spiking neural networks with Graphical Neural Activity Threads." arXiv preprint arXiv:2306.16684.
  • Walker E, JA Actor, C Martinez, N Trask. 2023. "Casual disentanglement of multimodal data." doi:10.2172/2431054.
  • Xu M, T Galanti, A Rangamani, L Rosasco, and T Poggio. 2023. “The Janus effect of SGD vs. GD: high noise and low rank.” CBMM Memo 144, MIT.
  • Yin R, Y Kim, Y Li, A Moitra, N Satpute, A Hambitzer, and P Panda. 2023. “Workload-Balanced Pruning for Sparse Spiking Neural Networks." arXiv preprint arXiv:2302.06746.
  • Zou Z and GE Karniadakis. 2023. “L-HYDRA: Multi-Head Physics-Informed Neural Networks.” arXiv preprint arXiv:2301.02152.

2022

  • Actor JA, A Huang, N Trask. 2022. "Polynomial Spline Networks with Exact Integrals and Convergence Rates." Proceedings of 2022 IEEE Symposium Series on Computational Intelligence. doi:10.1109/SSCI51031.2022.10022123
  • Kim Y, Y Li, H Park, Y Venkatesha, A Hambitzer and P Panda. 2022. “Exploring Temporal Information Dynamics in Spiking Neural Networks.” arXiv preprint arXiv:2211.14406.