2022

Contributed Presentations

• Fan T. “Expectation-Maximizing Probabilistic Partition Of Unity Networks with Applications in Quantum Computing,” World Congress in Computational Mechanics (WCCM) & Asian Pacific Congress on Computational Mechanics (APCOM), July-August 2022, Yokohama, Japan, (Virtual).
• Fan T, N Trask, M D’Elia, and E Darve. “Probabilistic Partition-Of-Unity Networks for High-Dimensional Regression Problems.” Berkeley/Stanford CompFest, December 10, 2022, Stanford, CA.
• Darve E. “Probabilistic Partition-of-Unity-Nets for High-Dimensional Regression,” Sandia National Laboratory, May 27, 2022, Livermore, CA.
• Parks M. “On Neumann-type Boundary Conditions for Nonlocal Models”, Workshop on Theoretical and Applied Aspects for Nonlocal Models, Banff International Research Station for Mathematical Innovation and Discovery, July 18, 2022, Banff, Canada (Invited).
• Parks M. “On Neumann-type Boundary Conditions for Nonlocal Models”, 9th U.S. National Congress on Theoretical and Applied Mechanics, June 21, 2022, Austin, Texas  (Invited).
• Qadeer S. “A Spectral Approach for Time-dependent PDEs using Machine-learned Basis Functions,” SIAM Annual Meeting (AN22) July 12, 2022.
• Howard A. “Multifidelity Deep Operator Networks,” SIAM Annual Meeting (AN22) July 12, 2022.
• Howard A, J Dong, S Gallier, and P Stinis. “Physics-informed Machine Learing for Particle Stresses in Dense Suspensions,” APS Division of Fluid Dynamics, Indianapolis, IN, November 2022.
• Ainsworth M. “Galerkin Neural Network Approximation of Multiscale Problems,” SIAM Annual Meeting (AN22) July 12, 2022.
• Trask N. “Data-Driven Whitney Forms for Structure Preserving SciML,” SIAM Annual Meeting (AN22) July 13, 2022.
• Shukla K. “Physics-Informed Neural Network for Ultrasound Non-Destructive Quantification of Cracks and Microstructures in Materials” SIAM Annual Meeting (AN22) July 13, 2022.
• Maxey M. “Force Coupling Method for Particle-Laden Flows and Recent Applications,” 19th U.S. National Congress on Theoretical and Applied Mechanics (USNCTAM2022) June 19-24, 2022.
• D’Elia M. "Nonlocal Kernel Network (NKN): a Stable and Resolution-Independent Deep Neural Network," El Imaging 2022. January 24, 2022.
• Howard A. “Learning Unknown Physics of Non-Newtonian Fluids.” SIAM PNW Conference, May 21, 2022. (Virtual).
• Howard A. “Multifidelity Nonlinear Operator Learning,” Pacific Northwest National Laboratory, TechFest, June 9, 2022. (Virtual).
• Qadeer S. “A Spectral Approach for Time-dependent PDEs using Machine-learned Basis Functions," Pacific Northwest National Laboratory, TechFest, June 9, 2022. (Virtual)

Organized Conferences & Mini-Symposiums

• Co-organized Symposium: Martin M. “From Stokesian Suspension Dynamics to Particulate in Turbulence,” IUTA Symposium, IMFT, Toulouse, France, August 29-September 2, 2022.

2021

Presentations

• Bochev, P. Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering and Technology (MMLDT-CSET) Conference. San Diego, September 2021.  
• Darve, E. “Physics-informed machine learning: open mathematical questions,” Seminar at the Simons Foundation, March 9, 2021.
• Darve E. “2nd order optimizers for physics-informed learning,” Center for Mathematics and Artificial, George Mason Colloquium, February 19, 2021.
• Darve E, M Forghani, Y Qian, and P Kitanidis. “Data Assimilation and Inverse Modeling using Variational Supervised-Encoders,” SIAM Computer Science and Engineering Conference, March 4, 2021.
• Darve E and K Xu. “Second-order physics constrained learning,” Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology an IACM Conference, San Diego, CA, September 26-29, 2021.
• D’Elia M. “Data Driven Learning of Nonlocal Models: from MD Simulations to Continuum Mechanics Models,” NM Machine Learning Symposium, Sandia National Laboratories, February 23, 2021.
• D’Elia M. “A General Framework for Nonlocal Domain Decomposition,” SIAM Computational Science and Engineering Conference, March 2021.
• D’Elia M. "Data Driven Learning of Nonlocal Models: from Molecular Dynamics to Continuum Mechanics,”  NM Machine Learning Symposium, Sandia National Laboratories, March 2021.
• D’Elia M. “A fractional model for anomalous diffusion with increased variability. Analysis, algorithms and applications to interface problems,” SIAM MS, June 17–18, 2021.
• D’Elia M. “Data-driven learning of nonlocal models: from high-fidelity simulations to constitutive laws,” Barret Lectures 2021, University of Tennessee, Knoxville. June 17–19, 2021.
• D’Elia M. "Nonlocal models in image processing: denoising and classification," IFIP 2021, August 30–September 3, 2021.
• D’Elia M. “Data driven learning of nonlocal models: from high-fidelity simulations to constitutive models,” Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology (MMLDT). September 27, 2021.
• D’Elia M. “Data driven learning of nonlocal models,” Nonlocality in Analysis, Numerics and Applications. October 27, 2021.
• D’Elia M. “Data driven learning of nonlocal models,” Mini-symposium on Peridynamic Modeling of Materials’ Behavior. International Mechanical Engineering Congress & Exposition (IMECE). November 1, 2021.
• D’Elia M. “On the prescription of nonlocal boundary conditions.” Mini-symposium on Peridynamic Modeling of Materials’ Behavior. International Mechanical Engineering Congress & Exposition (IMECE). November 2, 2021.
• Forghani M, Y Qian, JH Lee, M Farthing, T Hesser, PK Kitanidis, and EF Darve. “Deep Learning for Large-Scale Riverine Surface Flow Velocity,” SIAM Computer Science and Engineering Conference, March 3, 2021.
• He Q. “Track 2: Scientific and Engineering Digital Twins; Track 4: Reduced-order Modeling for Fluids, Solids, and Structures”, IACM Conference: Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology, San Diego, September 2021.
• Karniadakis GE. “From PINNs to DeepOnets: Approximating Functions, Functionals, and Operators using Deep Neural Networks,” University of Massachusetts, Amherst, February 9, 2021.
• Karniadakis GE. “PINNs & DeepOnets: Approximating Functions, Functionals, and Operators using Deep Neural Networks for Diverse Applications,” Free University of Berlin, February 28, 2021.
• Karniadakis GE. “DeepOnet: Learning Linear, Nonlinear and Multiscale Operators using Deep Neural Networks Based on the Universal Approximation Theorem of Operators,” SIAM/CSE, March 5, 2021.
• Karniadakis GE. “From PINNs to DeepOnets: Approximating Functions, Functionals, and Operators using Deep Neural Networks,” University of Iowa, March 12, 2021.
• Karniadakis GE. “From PINNs to DeepOnets: Approximating Functions, Functionals, and Operators using Deep Neural Networks,” Princeton Plasma Physics Laboratory, March 17, 2021.
• Karniadakis GE. “Physics-informed Neural Networks (PINNs) and Operator Regression (DeepOnet) for Diverse Applications,” Hitachi, March 18, 2021.
• Karniadakis GE. “Approximating Functions, Functionals, and Operators using Deep Neural Networks for Diverse Applications,” University of Cambridge, April 7, 2021.
• Karniadakis GE. “Parallel Physics-informed Neural Networks (PINNs) via Domain Decomposition,” AMID, Inc., April 23, 2021.
• Karniadakis GE. “Approximating Functions, Functionals, and Operators using Deep Neural Networks for Diverse Applications,” Lawrence Livermore National Laboratory, May 4, 2021.
• Karniadakis GE. “A Seamless Multiscale Operator Neural Network for Inferring Bubble Dynamics,” MNF2021, Imperial College. May 24, 2021.
• Qian Y, M Forghani, J Lee, M Farthing, T Hesser, PK Kitanidis, and E F Darve. “Bayesian Inference of Stochastic Differential Equations: with Application to Hamiltonian System,” SIAM Computer Science and Engineering Conference, March 3, 2021.
• Parks M. “nPINNS: Nonlocal Physics-Informed Neural Networks,” One Nonlocal World, January 2021. 
• Perego M. “Modeling Land Ice with Deep Operator Networks,” SIAM Southeastern Atlantic Section Conference, September 2021. 
• Parks M. “nPINNS: Nonlocal Physics-Informed Neural Networks,” 16th U.S. National Congress on Computational Mechanics, Chicago, Illinois, July 29, 2021.
• Xu K. and EF Darve. “ADCME: A General Framework of Machine Learning for Computational Engineering,” SIAM Computer Science and Engineering Conference, March 1, 2021.
• You H. “Data-Driven Learning of Nonlocal Models,” East Coast Optimization Meeting, George Mason University, April 2, 2021.

• You H. “Data-driven learning of nonlocal models: from high-fidelity simulations to constitute laws," SIAM MS, June 17-18, 2021.

Organized Conferences & Mini-Symposiums

• Organized mini-symposium: Biswas A, M Parks, and P Radu, “Nonlocal Operators and Machine Learning in Multiscale Modeling,” Mechanistic Machine Learning and Digital Twins for Computational Science, San Diego, CA, September 2021.
• Organized mini-symposium: Biswas A, M Parks, and P Radu, “Nonlocal Models in Continuum Mechanics: Mathematical, Computational, Machine Learning Aspects,” 16th U.S. National Congress on Computational Mechanics, Chicago, IL, July 2021.
• Organized mini-symposium: Bochev P, P Kuberry, and B Paskaleva. "Data Driven Approaches for Circuit Design and Analysis," Mechanistic Machine Learning and Digital Twins for Computational Science, San Diego, CA, September 2021.
• Organized Symposium: Darve E and HL Jonghyun. “Combining Artificial Intelligence and Machine Learning with Physical Sciences,” AAAI 2021 Spring Symposium Series, Palo Alto, CA, March 22–24, 2021.
• Organized Symposium: Darve E. “Combining Artificial Intelligence and Machine Learning with Physical Sciences,” AAAI 2021 Spring Symposium Series, Palo Alto, CA, March 22–24, 2021.
• Co-organizer and scientific committee member: Darve E. 1st IACM Conference for Machine Learning, and Digital Twins for Computational Science and Engineering, San Diego, CA, October 2021.
• Organized mini-symposium: D’Elia M. “Nonlocal Interface Problems for the Simulation of Heterogeneous Materials and Media,” Coupled Problems 2021, Chia Laguna, Italy, June 13-16, 2021.
• Organized invited special session: D’Elia M. “Nonlocal interface problems for the simulation of heterogeneous materials and media,” Coupled Problems 2021, Chia Laguna, Italy, June 13-16, 2021.
• Organized conference: D’ Elia M. “Nonlocal codes," One Nonlocal World and JPER (Journal of Peridynamics and Nonlocal Modeling) joint event, December 2 and 7, 2021.
• Organized conference: D’Elia M, Q Du, P Radu, P Seleson, X Tian, Y Yu, One Nonlocal World Opening Event, Virtual, January 2021.
• Organized conference: D’Elia M, Q Du, E Madenci, P Radu, P Seleson, S Silling, X Tian, Y Yu, Nonlocal Codes, A One Nonlocal World Project event, Virtual, December 2021.
• Organized mini-symposium: D’Elia M and C Glusa. “Model Learning and Optimization for Nonlocal and Fractional Equations,” SIAM Computer Science and Engineering Conference, Fort Worth, TX, March 2021.
• Organized conference: D’Elia M, M Gunzburger, and G Rozza. “RAMSES: reduced order models, approximation, surrogates, emulators and simulators,” SISSA, Italy. December 2021.
• Organized mini-symposium: D’Elia M, H Li, P Radu, P Seleson, Y Yu. “Nonlocality in Data-driven and Physics-based Materials Modeling.” SIAM MS, June 17-18, 2021.
• Organized mini-symposium: D’Elia M and E Otarola. “Optimal Control and Optimization for nonlocal and fractional equations”, IFIP 2021, Quito, Ecuador, Aug 30 – Sept 3, 2021.
• Organized conference: D’Elia M, G Rozza, M Gunzburger. "RAMSES: Reduced order models; Approximation theory; Machine learning; Surrogates, Emulators and Simulators", SISSA, Trieste, Italy. December 2021.
• Organized mini-symposium: D’Elia M, P Seleson. “Computational aspects of nonlocal models,” WCCM, World Congress on Computational Mechanics, Paris, France, January 2021.
• Organized mini-symposium: D’Elia M, P Seleson, “Local-to-Nonlocal and Nonlocal-to-Nonlocal Coupling Methods: Advances in Coupling Techniques and Treatment of Interfaces in Nonlocal Mechanics and Diffusion,” 16th U.S. National Congress on Computational Mechanics, Chicago, IL, July 2021.
• Organized mini-symposium: D’Elia M, P Seleson, “Local-to-Nonlocal and Nonlocal-to-Nonlocal Coupling Methods: Advances in Coupling Techniques and Treatment of Interfaces in Nonlocal Mechanics and Diffusion,” SIAM Conference on Mathematical Aspects of Material Science, Bilbao, Spain, July 2021.
• Organized mini-symposium: D’Elia M, P Seleson, and Y Yu. "Nonlocal Models in Computational Science and Engineering," SIAM Computational Science and Engineering Conference, Fort Worth, TX, March 2021.
• Organized mini-symposium: D’Elia M, N Trask, Y Yu. “Identifying constitutive behavior and dynamics via physics informed machine learning,” Mechanistic Machine Learning and Digital Twins for Computational Science, San Diego, CA, September 2021.
• Organized mini-symposium: He Q, P Gao, A Howard, J Li, “Integration of Models, Data and Artificial Intelligence for Energy and Power Systems,” Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology (MMLDT-CSET Conference), San Diego, CA, September 2021.
• Organized mini-symposium: Karniadakis GE. "Physics-Informed Neural Networks PINNs and DeepOnet: Theory and Applications," Mechanistic Machine Learning and Digital Twins for Computational Science, San Diego, CA, September 2021.
• Organized mini-symposium: Karniadakis GE. "Physics-Informed Neural Networks PINNs and DeepOnet: Theory and Applications," AmeriMech Symposium, Sponsored by the U.S. National Committee for Theoretical and Applied Mechanics, October 2021.
• Organized mini-symposium: Trask N, R Patel, N Nelson, “Machine Learning for Surrogate Model and Operator Discovery,” SIAM Conference on Computational Science and Engineering, Fort Worth, TX, March 2021.
• Organized mini-symposium: Trask N, NH Nelson, and RG Patel “Learning Operators From Data,” SIAM  Computational Science and Engineering Conference, March 2021.

contributed presentations

• Atzberger PJ and R Lopez. “Variational Autoencoders with Manifold Latent Spaces for Learning Nonlinear Dynamics,” SMB, June 2021.
• Atzberger PJ and R Lopez. “Variational Autoencoders for Learning Nonlinear Dynamics of Physical Systems,” Workshop on Mathematical Machine Learning and Application, Penn. State University Park, PA, December 2020.
• Atzberger PJ. “Tutorial on using USER-SELM for fluctuating hydrodynamics models in LAMMPS,” LAMMPS Workshop, break-out session, August 2021.
• Atzberger PJ. “USER-MLMOD Package: Machine Learning Methods for Data-Driven Models in LAMMPS,” LAMMPS Workshop, August 2021.
• Atzberger PJ. “Variational Autoencoders with Manifold Latent Spaces for Learning Nonlinear Dynamics,” Sandia Machine Learning and Deep Learning Workshop 2021 (MLDL2021), July 2021.
• Atzberger PJ. “Surface Fluctuating Hydrodynamics Methods for the Drift-Diffusion Dynamics of Particles and Microstructures within Lipid Bilayer Membranes,” 16th US National Congress on Computational Mechanics (USNCCM16), July 2021.
• Darve E. “Machine learning for inverse modeling in mechanics,” Artificial Intelligence for Robust Engineering & Science, AIRES 2: Machine Learning for Robust Digital Twins, January 2021.
• Darve E. “Deep Neural Networks for Inverse Modeling,” SIAM/CAIMS Annual Meeting (AN20) July 2021.
• Fan T, K Xu, J Pathak, and E Darve. “Solving Inverse Problems in Steady-State Navier-Stokes Equations using Physics Constrained Machine Learning,” World Congress in Computational Mechanics (WCCM)—European Community on Computational Methods in Applied Sciences (ECCOMAS) Joint Congress.
• Gulian M. “A Unified Theory of Fractional, Nonlocal, and Weighted Nonlocal Vector Calculus," One Nonlocal World, Virtual Poster Presentation, Virtual, January 22, 2021.
• Howard A. “Learning a non-local model for non-Newtonian fluid rheology,” Nonlocal World, Virtual Poster Presentation, January 22, 2021. 
• Jagtap AD. "Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations," AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning in Physics Sciences, Stanford University, Palo Alto, California, Virtual, March 22, 2021.
• Meng XH. “Multi-fidelity Bayesian neural networks for inverse PDE problems with noisy data,” SIAM Annual Meeting (AN21), Virtual, July 19-23, 2021.
• Meng XH. “Multi-fidelity Bayesian neural networks for inverse PDE problems with noisy data,” 16th U.S. Association for Computational Mechanics, Virtual, July 25-29, 2021.
• Patel R. “Learning continuum-scale models from micro-scale dynamics via Operator Regression,” 14th World Congress in Computational Mechanics, Virtual, January 2021. 
• Patel R. “Multiscale training for Physics-informed Neural Networks,” 20th Copper Mountain Conference on Multigrid Methods, Virtual, March 2021. 
• Xu K and E Darve. “Data-Driven Inverse Modeling and Deep Learning with Incomplete Observations,” 14th World Congress on Computational Mechanics (WCCM) meeting, Paris, France, January 11-15, 2021.

2020

Presentations

• Atzberger PJ and P Stinis "Developments in Machine Learning: Foundations and Applications - Parts I - III," SIAM Annual Meeting 2020.
• Bochev PB. "Data-Driven Approaches for Circuit Design and Analysis," SIAM Conference on Mathematics of Data Science (MDS20), Cincinnati, OH, May 5-7, 2020.
• Bochev PB. "Computational science in the national labs," SIAM Student Chapter Talk, Virginia Tech, December 2, 2020.
• Bochev, PB. “Development of data-driven models for radiation-induced photocurrent effects,” MLDL Workshop, Sandia National Laboratories, August 2020.
• Bochev, PB. “Data-driven exponential integrators for parabolic PDEs”, Eringen Medal Symposium at SES-2020, September 2020.
• Darve E, G Wang, and A Lew. “Combinatorial PDE-constrained Control with Deep Reinforcement Learning,” Machine Learning in Science & Engineering, December 14, 2020.
• Darve E, G Wang, and A Lew. “Combinatorial PDE-constrained Control with Deep Reinforcement Learning,” Workshop on the Mathematical Machine Learning and Application, December 14, 2020.
• Daskalakis C. "Game Theory and Machine Learning," Max Planck Institute for Intelligent Systems Machine Learning Summer School, Tübingen, Germany, July 2020.
• Daskalakis C. "How Computer Science is Changing the World," Public Lecture. Democritus University of Thrace, Xanthi, Greece, January 2020.
• Daskalakis C. "Learning from Biased Data," National Centre of Scientific Research “Demokritos'' Summer School, Athens, Greece, July 2020.
• Daskalakis C. "The Promise and Threat of Artificial Intelligence," Public Lecture. Eugenides Foundation, Athens, Greece, January 2020.
• Daskalakis C. "Three ways Machine Learning fails and what to do about them," Columbia University Computer Science Distinguished Lecture, New York, NY, September 2020.
• D'Elia M. "nPINNs: Nonlocal Physics-Informed Neural Networks," Presented poster in collaboration with G Pang, G Karniadakis, and M Parks, CoDA 2020, Santa Fe, NM, February 25-27, 2020.
• D'Elia M. "Nonlocal Physics-Informed Neural Networks - A Unified Theoretical and Computational Framework for Nonlocal Models," AAAI-MLPS 2020: AAAI 2020 SPRING SYMPOSIUM: Combining Artificial Intelligence and Machine Learning with Physical Sciences. March 23, 2020.
• D'Elia M. "Part A: Nonlocal models in computational science and engineering: theory and challenges." "Part B: Nonlocal Models in Computational Science and Engineering: treatment of interfaces in heterogeneous materials and media, image processing, and model learning," Summer School at University of Roma, La Sapienza, Rome, Italy, September 2020.
• Forghani M, Y Qian, J Lee, M Farthing, T Hesser, P Kitanidis, and E Darve. “Fast Solver of the Shallow Water Equations with Application to Estimation of the Riverine Surface Velocity,” APS Annual Meeting, November 22-24, 2020.
• Forghani M, Y Qian, J Lee, M Farthing, T Hesser, P Kitanidis, and E Darve. “Deep Learning Application to Fast Estimation of Riverine Surface Flow Velocity,” AGU 2020, December 1-17, 2020.
• Forghani M, J Lee, Y Qian, M Farthing, T Hesser, P Kitanidis, and E Darve. “Application of Deep Learning to Large Scale Riverine Bathymetry and Surface Flow Velocity Estimation,” CMWR, Stanford University, December 14, 2020.
• Ghorbanidehno H, Y Qian, JH Lee, MW Farthing, T Hesser, PK Kitanidis, EF Darve, and M Forghani. 2020. "Deep learning techniques for nearshore and riverine bathymetry estimation using water-surface observations," Presented poster at the 2020 Oceans Sciences Meeting, AGU, San Diego, CA, February 16-21, 2020.
• Karniadakis, GE. “Physics-Informed Neural Networks PINNs and Applications,” Nanyang Technological University, Singapore, January 2020.
• Karniadakis, GE. “Physics-Informed Neural Networks PINNs and Applications,” National Science Foundation/SMU workshop, February 2020.
• Karniadakis, GE. “Physics-Informed Neural Networks PINNs and Applications,” Applied Mathematics, South Methodist University, February 2020.
• Karniadakis, GE. “Physics-Informed Neural Networks PINNs and Applications,” ANSYS, Inc., April 2020.
• Karniadakis, GE. “Physics-Informed Neural Networks PINNs and Applications,” Applied Mathematics, ETH, May 2020.
• Karniadakis, GE. “Physics-Informed Neural Networks PINNs and Applications,” Politecnico Di Milano, MOX, June 2020.
• Karniadakis, GE. “Physics-Informed Neural Networks PINNs and Applications,” Siemens Corporation, August 2020.
• Karniadakis, GE. “Physics-Informed Neural Networks PINNs and Applications,” South Eastern University, Nanjing, China, August 2020.
• Karniadakis, GE. “Physics-Informed Neural Networks PINNs and Applications,” KDD2020, Earth Day, August 2020.
• Karniadakis, GE. “Physics-Informed Neural Networks PINNs and Applications,” Center for Brains, Mind and Machines (CBMM), Massachusetts Institute of Technology, Massachusetts, September 2020.
• Karniadakis, GE. “Physics-Informed Neural Networks PINNs and Applications,” Indian Institute of Technology, Roorkee (IITR), India, September 2020.
• Karniadakis, GE. “Physics-Informed Neural Networks PINNs and Applications,” The Oden Institute, University of Texas at Austin, Austin, Texas, September 2020.
• Paskaleva B and P Bochev. 2020. "Data-driven compact device models," Poster presented at the 2020 CoDA Conference, Santa Fe, NM, February 25-27, 2020.
• Qian Y, H Ghorbanidehno, M Forghani, MW Farthing, T Hesser, PK Kitanidis, and EF Darve. “Surfzone Topography-informed Deep Learning Techniques to Nearshore Bathymetry with Sparse Measurements,” AAAI-MLPS, Association for the Advancement of Artificial Intelligence, Spring Symposium Series Symposium, March 2020.
• Qian YH, JH Lee, M Forghani, M Farthing, T Hesser, PK Kitanidis, and EF Darve. "Deep Learning Based Spatial Interpolation Methods for Nearshore Bathymetry with Sparse Measurements." Computational Methods in Water Resources (CMWR), Stanford University, Stanford, CA, June 22-25, 2020.
• Stinis P. "Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning," AAAI 2020 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, March 2020.
• Trask N. "Physics-informed graph neural nets: A unification of NN architectures with mimetic PDE discretization," SIAM Annual Meeting, July 2020.
• Trask N. "Robust Training and Initialization of Deep Neural Networks: An Adaptive Basis Viewpoint," MSML2020 - Mathematical and Scientific Machine Learning Conference, Princeton, NJ, July 2020.
• Trask N., R.G. Patel, B.J. Gross, and P.J. Atzberger. "GMLS-Nets: Scientific Machine Learning Methods for Unstructured Data," AAAI-MLPS, Stanford, March 2020.
• Valiant G. "How bad is worst-case data if you understand where it comes from?" MIT MIFODS Workshop on Learning Under Complex Structure, Cambridge, MA, January 2020.
• Valiant G. "Sample Amplification." Workshop on data augmentation and equivariance, September 2020.
• Xu K and E Darve. "Data-driven Constitutive Relation Modeling using Deep Neural Networks," Thermal & Fluid Sciences Industrial Affiliates and Sponsors Conference 2020 at Stand University, Stanford, CA, February 3-5, 2020.
• Qian Y, H Ghorbanidehno, M Forghani, T Farthing, PK Hesser, PK Kitanidis, and EF Darve. "Surfzone Topography-informed Deep Learning Techniques to Nearshore Bathymetry with Sparse Measurements," AAAI-MLPS, Association for the Advancement of Artificial Intelligence, Spring Symposium Series symposium, March 24, 2020.
• Xu K. "Learning a Neural Network Monte Carlo Sampler for Stochastic Inverse Problems," Combing Artificial Intelligence and Machine Learning with Physics Sciences Symposium, Stanford University, March 23-25, 2020.
• Xu K and E Darve. “Data-driven Constitutive Relation Modeling using Deep Neural Networks,” Thermal & Fluid Sciences Industrial Affiliates and Sponsors Conference, Stanford University, California, February 2020.
• Xu K and E Darve. 2020. "Data-driven Inverse Modeling for Subsurface Properties with Physics-Based Machine Learning," SIAM Imaging Science Mini-symposium on UQ and ML for the Subsurface, Toronto, Ontario, Canada, July 6-9, 2020.

organized conferences & Mini-symposiums

• Organized Mini-symposium: Atzberger, PJ and P Stinis. “Developments in Machine Learning: Foundations and Applications - Parts I - III,” SIAM Annual Meeting, July 2020.
• Organized Symposium: Darve, E and JH Lee. “Combining Artificial Intelligence and Machine Learning with Physical Sciences,” AAAI 2020 Spring Symposium Series, 2020.

Contributed Presentations

• D’Elia M, G Pang, GE Karniadakis, and M Parks. “nPINNs: Nonlocal Physics-Informed Neural Networks,” poster at CODA 2020, Santa Fe, NM, February 2020.
• D'Elia M. "Data-driven Learning of Nonlocal Models: from High-fidelity Simulations to Constitutive Laws," Presented conference poster at the Workshop on Mathematical Machine Learning and Application, December 14-16, 2020 (poster presented).
• Fan T, K Xu, J Pathak, and E Darve, “Solving Inverse Problems in Steady-State Navier-Stokes Equations using
  Deep Neural Networks,” AAAI Fall 2020 Symposium on Physics-Guided AI to Accelerate Scientific Discovery,
  Aug. 2020.
• Ghorbanidehno J, HLM Farthing, T Hesser, P Kitanidis, E Darve, and M Forghani. “Deep learning techniques for nearshore and riverine bathymetry estimation using water-surface observations,” 2020 Oceans Sciences Meeting, AGU, San Diego, CA, February 2020.
• Paskaleva B and P Bochev. “Data-driven compact device models,” poster at CODA 2020, Santa Fe, NM, February 2020.
• Xu K, E Darve. “ADCME—Machine Learning for Computational Engineering,” Berkeley/Stanford CompFest,
  2020.

2019

Presentations

• Atzberger PJ "Challenges and Opportunities using Machine Learning Approaches in the Sciences and Engineering," Sandia National Laboratories, Albuquerque, NM, March 2019.
• Atzberger PJ. "Hydrodynamics of Curved Fluid Interfaces," International Congress on Industrial and Applied Mathematics (ICIAM), Valencia, Spain, July 2019.
• Atzberger PJ. "Incorporating Physics-Based Inductive Bias into Machine Learning Methods," Sandia National Laboratories, Livermore, CA, May 2019.
• Atzberger PJ. "Machine Learning Approaches for the Sciences and Engineering: Recent Developments," SIAM Computer Science and Engineering (CSE) Conference. 2019.
• Darve E. "Deep Neural Networks for Inverse Modeling," MIT Distinguished Seminar Series in Computational Science and Engineering, (Distinguished Seminar), November 21, 2019.
• Darve E and L Cambier. "Numerical Linear Algebra for Machine Learning," 2 parts; 8 speakers. SIAM Conference on Computational Science and Engineering (CSE), Spokane, WA, February 25-March 1, 2019.
• Daskalakis C. Greek Ministry of Defense, Athens, Greece, January 2019.
• Daskalakis C. National Technical University of Athens, Athens, Greece, January 2019.
• Daskalakis C. Public Lecture at Fundraising Gala, Not for Profit Organization "Mazi gia to paidi," Athens, Greece, January 2019.
• Daskalakis C. American College of Athens, Athens, Greece (Outreach Lecture), January 2019.
• Daskalakis C. Varvakeio high school, Athens, Greece (Outreach Lecture), January 2019.
• Daskalakis C. Columbia-Princeton Probability Day, Princeton, NJ (Plenary Talk), March 2019.
• Daskalakis C. Technology Forum, Thessaloniki, Greece (Plenary Talk), April 2019.
• Daskalakis C. Arsakeio High School, Athens, Greece (Outreach Talk), April 2019.
• Daskalakis C. Emmanuel Drandakis Lecture, Conference on Research on Economic Theory and Econometrics, Tinos, Greece, July 2019.
• Daskalakis C. AI Institute, Microsoft, Redmond, WA, August 2019.
• Daskalakis C. CS Colloquium, University of Wisconsin-Madison, Madison, WI, September 2019.
• Daskalakis C. Applied Math Colloquium, Brown University, Providence, RI, October 2019.
• Daskalakis C. Statistics Seminar, University of Pennsylvania, Philadelphia, PA, October 2019.
• Daskalakis, C. “Game Theory and Computation,” Fundraising Gala for Institut des Hautes ´Etudes Scientifiques (IHES), Harvard Club of NYC, New York, NY. (Public Lecture), November 2019.
• Daskalakis, C. “Learning from Censored and Dependent Data”, Brown University Kanellakis Lecture, Providence, RI. (Distinguished Lecture), December 2019.
• Daskalakis, C. “The Promise and Threat of Artificial Intelligence”, Concert Hall of Athens, Athens, Greece, (Public Lecture), December 2019.
• D'Elia M. "nPINNs: Physics-Informed Neural Networks," Optimal control and optimization for nonlocal models, Johann, Radon, Institute for Computational and Applied Mathematics (RICAM), Linz, Austria, Oct 2019.
• Forghani J., H.L.M. Farthing, T. Hesser, P. Kitanidis, and E. Darve. "Deep learning techniques for riverine bathymetry and flow velocity estimation," 2019 AGU Meeting, San Francisco, CA, December 9-13, 2019.
• Gross BJ, N Trask, P Kuberry, and PJ Atzberger. "Meshless Methods for Manifolds: GMLS Kernel Approximations of Hydrodynamic Responses in Curved Fluid Interfaces," 16th International Conference on Approximation Theory 2019 in Nashville, TN, May 2019.
• Gulian M. "Machine Learning of Space-Fractional Differential Equations," Optimal control and optimization for nonlocal models, Johann, Radon, Institute for Computational and Applied Mathematics (RICAM), Linz, Austria, Oct 2019.
• Gulian M. "Machine Learning of Linear Fractional Differential Equations," SIAM Computer Science and Engineering Conference, Spokane, WA, February 2019.
• Huang J. NVIDIA CEO addresses 1,400+ attendees of SC19, the annual supercomputing conference, in Denver, where he mentions PhILMs physics-informed DNN methods. Watch the video on YouTube. Jensen refers to Physics-Informed GANs at 14:50 min and at 42:50 min. PINNs = Physics-Informed Neural Networks. Read our paper that Jensen Huang mentioned in his SC19 talk on the expanding universe of HPC.
• Karniadakis GE. "Physics-Informed Neural Networks PINNs and Applications," University of Pennsylvania, December 2019.
• Karniadakis GE. Department of Mechanics, Zhejiang University, Zhejiang, China, May 13, 2019.
• Karniadakis GE. Department of Mechanical Engineering, Northwestern University, Evanston, IL, April 18, 2019.
• Karniadakis GE. Department of Mathematics, Technical University of Munich, Munich, Germany January 9, 2019.
• Karniadakis GE. Department of Mechanical Engineering, Stanford University, Stanford, CA, November 30, 2019.
• Karniadakis GE. DOE ASC meeting, Rockville, MD, January 30, 2019.
• Parks ML. "On Neumann-type Boundary Conditions for Nonlocal Models," The 9th International Congress on Industrial and Applied Mathematics, Valencia, Spain, July 2019.
• Parks ML. "On Neumann-type Boundary Conditions for Nonlocal Models," The 5th Annual Meeting of SIAM Central States Section, Ames, IA, October 19, 2019.
• Patel R, PJ Atzberger, N Trask, and E Cyr. "Operator Regression for PDE Discovery," Poster presented at the Deep Learning for Science School in Berkeley, CA, July 2019.
• Perego M. "Neural Network Surrogates of PDE-based Dynamical Systems, Application to Ice Sheet Dynamics," SIAM Computer Science and Engineering Conference, Spokane, WA, February 2019.
• Qian Y. "Applications of Deep Neural Network to Near-shore Bathymetry with Sparse Measurements," AGU 2019 Fall Meeting, San Francisco, CA, December 9-13.
• Stinis P. "Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks," ICERM Workshop on Scientific Machine Learning, Providence, RI, January 2019.
• Tartakovsky AM. "Learning Parameters and Constitutive Relationships with Physics- Informed Deep Neural Networks," INTERPORE 11th Annual Meeting, Valencia, Spain.
• Tartakovsky AM. "Learning Parameters and Constitutive Relationships with Physics Informed Deep Neural Network," Computational Science and Engineering (CSE), Spokane, WA.
• Tartakovsky AM. "Physics-Informed Machine Learning," Seminar at Portland State University, Portland, OR.
• Trask N. "GMLS-Nets: A machine learning framework for unstructured data," Neural Information Processing Systems, Vancouver, Canada, December 2019.
• Trask N, RG Patel, BJ Gross, and PI Atzberger. "GMLS-Nets: Scientific Machine Learning Methods for Unstructured Data," NeurIPs 2019: Workshop on Machine Learning and the Physical Sciences, Vancouver, Canada, December 19, 2019.
• Valiant G. 2019. "New Problems and Perspectives on Learning, Sampling, and Memory, in the Small Data Regime," Princeton Theory Seminar, Princeton, NJ, October 2019.
• Xu K. 2019. "Adversarial Numerical Analysis," Presented a poster at Xpo.

organized conferences & Mini-Symposiums

• Organized Mini-symposium: Gross B.J. and Atzberger P.J. “Machine Learning Approaches for the Sciences and Engineering: Recent Developments,” SIAM Conference on Computational Science and Engineering (CSE), Spokane, Washington, February 2019.

• Organized Mini-symposium: Howard, A.A. and W.S. Rosenthal. “Mathematical Modeling for Multiphase Flow,” SIAM Pacific Northwest Section Meeting, Seattle, WA, October 2019.

• Organized international conference: D’Elia M. et al. “Optimal control and optimization for nonlocal models,” part of Semester on optimization, RICAM, Linz, Austria, October 2019.

Contributed Presentations

• Forghani, J, HLM. Farthing, T Hesser, P Kitanidis, E Darve. 2019. “Deep learning techniques for riverine bathymetry and flow velocity estimation,” AGU Meeting, San Francisco, CA, December 2019.
• He, Q, G Tartakovsky, D Barajas-Solano, A Tartakovsky. “Physics-Informed Deep Neural Networks for Multiphysics Data Assimilation in Subsurface Transport Problems,” American Geophysical Union (AGU) Fall Meeting 2019, San Francisco, CA, December 2019.
• Trask N, RG Patel, BJ Gross, and PJ Atzberger. "GMLS-Nets: Scientific Machine Learning Methods for Unstructured Data,”NeurIPs 2019: Workshop on Machine Learning and the Physical Sciences, Vancouver, Canada, December 2019.

2018

Presentations

• Daskalakis C. Theory of Computation Colloquium, MIT, Cambridge, MA, September 2018.
• Daskalakis C. Applied Mathematics Seminar, MIT, Cambridge, MA, October 2018.
• Daskalakis C. Workshop on Complexity of Total Search Problems, Foundations of Computer Science Conference, Paris, France, October 2018.
• Daskalakis C. Algorithms Seminar, Boston University, Boston, MA, November 2018.
• Daskalakis C. New York Colloquium on Algorithms and Complexity, New York, NY, November 2018.
• Daskalakis C. ECE Seminar, UT Austin, Austin, TX, November 2018.
• Daskalakis C. Applied Economic Theory Seminar, University of Chicago, Chicago, IL, December 2018.
• Daskalakis C. Greek Stochastics Kappa Workshop, Athens, Greece, December 2018.
• Tartakovsky AM. "Physics-Informed Machine Learning methods for Parameter and Model Estimation and Uncertainty Reduction," CCMA Seminar at Pennsylvania State University, State College, PA, 2018.
• Tartakovsky AM "Non-local mesoscale multiphase flow model," seminar at Imperial College, London, 2018..