# Presentations

## 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). - 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

##### 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,” M
*echanistic 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,” 1
*6th 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.
*SIAM Annual Meeting (AN21),*Virtual, July 19-23, 2021. - Meng XH. “Multi-fidelity Bayesian neural networks for inverse PDE problems with noisy data,” 1
*6th 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 2
*020 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,” A
*AAI-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..