Publications

2025

  • 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.
  • 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.

2024

  • 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.
  • 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 W, AA Howard, and P Stinis. 2024. "Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks." arXiv preprint arXiv:2407.01613
  • 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. arXiv preprint arXiv:2311.07790.
  • Chen Y, H Owhadi, and F Schäfer. 2024. “Sparse Cholesky Factorization for Solving Nonlinear PDEs via Gaussian Processes.” Mathematics of Computation. doi:10.1090/mcom/3992.
  • 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, 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.
  • Hu Z, Z Zhang, GE Karniadakis, and K Kawaguchi. 2024. “Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker-Planck Equations.” arXiv preprint arXiv:2402.07465.
  • 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.
  • Kopaničáková A and GE Karniadakis. 2024. “Deeponet based preconditioning strategies for solving parametric linear systems of equations.” arXiv preprint arXiv:2401.02016.
  • 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.
  • Kumar V, S Goswami, K Kontolati, MD Shields, and GE Karniadakis. 2024. “Synergistic Learning with Multi-Task DeepONet for Efficient PDE Problem Solving.” arXiv preprint arXiv:2408.02198.
  • 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.
  • Oommen V, A Bora, Z Zhang, and GE Karniadakis. 2024. “Integrating Neural Operators with Diffusion Models Improves Spectral Representation in Turbulence Modeling.” arXiv preprint arXiv:2409.08477. 
  • 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.
  • Sanderse B, P Stinis, R Maulik, SE Ahmed. 2024. “Scientific machine learning for closure models in multiscale problems: A review.” arXiv preprint arXiv:2403.02913. 
  • 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.
  • 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, Z Zhang, and GE Karniadakis. 2024. “SympGNNs: Symplectic Graph Neural Networks for identifying high-dimensional Hamiltonian systems and node classification.” arXiv preprint arXiv:2408.16698.
  • 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
  • Wang T, Z Hu, K Kawaguchi, Z Zhang, and GE Karniadakis. 2024. “Tensor neural networks for high-dimensional Fokker-Planck equations.” arXiv preprint arXiv:2404.05615.
  • 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.
  • Zhuang Q, CZ Yao, Z Zhang, and GE Karniadakis. 2024. “Two-scale Neural Networks for Partial Differential Equations with Small Parameters.” arXiv preprint arXiv:2402.17232.
  • Zou Z, A Kahana, E Zhang, E Turkel, R Ranade, J Pathak, and GE Karniadakis. 2024. “Large Scale Scattering Using Fast Solvers Based on Neural Operators.” arXiv preprint arXiv:2405.12380.
  • 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

  • Anagnostopoulos SJ, JD Toscano, N Stergiopulos, and GE Karniadakis. 2023. “Residual-Based Attention and Connection to Information Bottleneck Theory in PINNs.” arXiv preprint arXiv:2307.00379.
  • 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.
  • Hu Z, Z Yang, Y Wang, GE Karniadakis, and K Kawaguchi. 2023. “Bias-variance trade-off in physics-informed neural networks with randomized smoothing for high-dimensional PDEs.” arXiv preprint arXiv:2311.15283.
  • 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.
  • Yang X, H Owhadi. 2023. “A Mini-Batch Method for Solving Nonlinear PDEs with Gaussian Processes.” arXiv preprint arXiv:2306.00307.
  • 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.
  • Zhang Q, C Wu, A Kahana, Y Kim, Y Li, GE Karniadakis, and P Panda. 2023. "Artificial to spiking neural networks conversion for scientific machine learning." arXiv preprint arXiv:2308.16372.
  • 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.