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The Crunch Group The collaborative research work of George Em Karniadakis
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  4. Machine Learning + X Seminars 2022

Machine Learning + X Seminars 2022

  • December 30 Recording
  • December 30, 2022: Transformer for Partial Differential Equations’ Operator Learning by Oded Ovadia, Tel Aviv University
  • December 30, 2022: Convergence analysis of the Multi-scale Deep Neural Network (MscaleDNN) by Bo Wang, Hunan Normal University
  • December 23 Recording
  • December 23, 2022: A Dimension-Augmented Physics-Informed Neural Network (DaPINN) with High Level Accuracy and Efficiency by Kamaljyoti Nath, Brown University
  • December 23, 2022: Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev Spaces by Jonathan Siegel, Texas A&M University
  • December 16 Recording
  • December 16, 2022: Physics-informed neural network for charged particles surrounded by conductive boundaries by Fateme Hafezi, Institute for Advanced Studies in Basic Sciences, Iran
  • December 16, 2022: Reliable extrapolation of deep neural operators informed by physics or sparse observations by Min Zhu, University of Pennsylvania
  • December 9 Recording
  • December 9, 2022: The Mori-Zwanzig formulation of deep learning by Xiantao Li, Pennsylvania State University
  • December 9, 2022: Energetically consistent model reduction for Hamiltonian and metriplectic systems by Anthony David Gruber, Sandia National Laboratories
  • December 2 Recording
  • December 2, 2022: DASA-PINNs: Differentiable adversarial self-adaptive pointwise weighting scheme for physics-informed neural networks by Guangtao Zhang, University of Macau
  • December 2, 2022: On the Activation Function Dependence of the Spectral Bias of Neural Networks by Adar Kahana, Brown University
  • November 25 Recording
  • November 25, 2022: Learning to Generate, Edit, and Arrange 3D Shapes by Srinath Sridhar, Brown University 
  • November 25, 2022:  TransPolymer: a Transformer-based Language Model for Polymer Property Predictions by Changwen Xu, Carnegie Mellon University
  • November 18 Recording
  • November 18, 2022: GeONet: a neural operator for learning the Wasserstein geodesic by Xiaohui Chen, University of Illinois Urbana-Champaign
  • November 18, 2022: Self-Validated Physics-Embedding Network (SVPEN): A general framework for inverse problems by Ruiyuan Kang, Dimitrios Kyritsis, Khalifa University, Abu Dhabi, UAE
  • November 11 Recording
  • November 11, 2022: Challenges of neural PDE surrogates by
    Max Welling & Johannes Brandstetter, Microsoft Research, Amsterdam
  • November 11, 2022: Towards Deep Learning for Wind Farm Wake Modeling by Suraj Pawar & Ashesh Sharma, Shell Global Solutions & National Renewable Energy Laboratory
  • November 4 Recording
  • November 4, 2022: A Review of Diffusion Models by Leonard Gleyzer, Brown University
  • November 4, 2022: From GRAND to GRAFF: PDE and energy-based learning algorithms for graph neural networks by Zhen Zhang, Quercus Hernandez, Brown University
  • October 28 Recording
  • October 28, 2022: Towards Responsible AI via Self-Healing Inference and Tensor-Compressed Training by Zheng Zhang, University of California, Santa Barbara
  • October 28, 2022: Presentation of “ A Robust Scientific Machine Learning for Optimization: A Novel Robustness Theorem ” by Queroz et al. by Jerome Darbon, Brown University
  • October 21 Recording
  • October 21, 2022: Discontinuity Computing Using Physics-Informed Neural Networks by Li Liu, Institute of Applied Physics and Computational Mathematics, China
  • October 21, 2022: Exact conservation laws for neural network integrators of dynamical systems by Eike Mueller, University of Bath, UK
  • October 14 Recording
  • October 14, 2022: Reconstructing the pressure field around a swimming fish using a physics-informed neural network by Michael Calicchia, John Hopkins University
  • October 14, 2022: Is L2 Physics Informed Loss Always Suitable for Training Physics Informed Neural Network? by Shanda Li, Carnegie Mellon University
  • October 7 Recording
  • October 7, 2022: Physics-Informed Neural Networks for Shell Structures by Jan-Hendrik Bastek, Dennis M. Kochmann, ETH Zürich
  • October 7, 2022: Deep learning tools tutorial: neural network intelligence (NNI) by Zongren Zou, Brown University
  • September 30 Recording
  • September 30, 2022: Can a Fruit Fly Learn Word Embeddings? by Oded Ovadia, Tel Aviv University
  • September 30, 2022: UCNS3D- An open-source high-order compressible flow solver for unstructured meshes: philosophy and future by Panagiotis Tsoutsanis, Cranfield University
  • September 23 Recording
  • September 23, 2022: A Hybrid Iterative Numerical Transferable Solver (HINTS) for PDEs Based on Deep Operator Network and Relaxation Methods by Enrui Zhang, Adar Kahana, Brown University
  • September 23, 2022: A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level by Qian Zhang, Brown University
  • September 16 Recording
  • September 16, 2022: Deep learning-based reduced order models for parametrized PDEs by Stefania Fresca, Politecnico di Milano
  • September 16, 2022: I-FENN: Integrated Finite Element Neural Network. Application to continuum damage mechanics of quasi-brittle materials by Panos Pantidis, New York University
  • September 9 Recording
  • September 9, 2022: Predicting Wind-Driven Spatial Deposition through Deep Autoencoders in a Complex Terrain by Giselle Fernandez, Lawrence Livermore National Laboratory
  • September 9, 2022: Physics-informed neural network forward and inverse modeling of multiphase poromechanics using stress-split sequential training by Ehsan Haghighat, Carbon3D
  • September 1 Recording
  • September1, 2022: Open Access Benchmark Datasets and Metamodels for Problems in Mechanics by Emma Lejeune, Boston University
  • September 1, 2022: A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks by Chenxi Wu, Brown University
  • August 26 Recording
  • August 26, 2022: Reconstructing cell phenotypic transition dynamics from single cell data by Jianhua Xing, University of Pittsburgh
  • August 26, 2022: Physics-informed, noise-aware, and equality Constrained NN (Forward/Inverse Problems with Multi-fidelity data) by Shams Basir, University of Pittsburgh
  • August 19 Recording
  • August 19, 2022: PINN Training using Biobjective Optimization: The Trade-off between Data Loss and Residual Loss by Kathrin Klamroth, Matthias Ehrhardt, University of Wuppertal
  • August 19, 2022: Data-driven discovery of Fokker-Planck equation for the Earth’s radiation belts electrons using Physics-Informed Neural Networks by Enrico Camporeale, University of Colorado
  • August 12 Recording
  • August 12, 2022: Machine learning for Fluid Mechanics by Steven L. Brunton, University of Washington
  • August 12, 2022: Designing and Developing Physics-Informed Machine Learning Models and Methods for Reusing Power System Components by Federica Bragone, KTH Royal Institute of Technology
  • August 5 Recording
  • August 5, 2022: From Real-time Optimal Control to Stiff Chemical Kinetics: Merging Physics Informed Neural Networks and Theory of Functional Connections for fast and accurate solutions of ODEs by Roberto Furfaro, University of Arizona
  • August 5, 2022: Accelerated training of Physics-informed Neural Networks (PINNs) using Meshless Discretization by Varun Shankar, University of Utah
  • July 29 Recording
  • July 29, 2022: Recurrence CFD: Fast, data-assisted simulations of flows with multiple time scales by Thomas Lichtenegger, Johannes Kepler University
  • July 29, 2022: Solving Quantitative Reasoning Problems with Language Models by Qian Zhang, Brown University
  • July 22 Recording
  • July 22, 2022: Understanding the origin of El Niño in El Niño-Southern Oscillation model and the forecasting of Sea surface temperature using ensemble deep learning model by Arnob Ray, Indian Statistical Institute
  • July 22, 2022: Constructing robust high order entropy stable discontinuous Galerkin methods by Jesse Chan, Rice University
  • July 22, 2022: Learning PINNs through the Van der Pol Equation by Theodore Tellides, Brown University
  • July 22, 2022: Paradigm shift from multiscale modeling, UQ and fractional calculus to scientific machine learning by He Li, Brown University
  • July 15 Recording
  • July 15, 2022: How a bio-inspired olfactory learning algorithm can be implemented 200x faster using Actors instead of Spiking Neural Networks by Srinivas Vamsi Parasa, Intel Corporation
  • July 15, 2022: Physics Informed Adversarial Training for Solving Partial Differential Equations by Simin Shekarpaz, Brown University
  • July 7 Recording
  • July 7, 2022: Deep-learning-based short-term electricity load forecasting: A real case application by Ibrahim Yazici, Istanbul Technical University
  • July 7, 2022: Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture by Yicheng Wang, Texas A&M University
  • July 1 Recording
  • July 1, 2022: Generative modeling of turbulence by Hanno Gottschalk, University of Wuppertal
  • July 1, 2002: Learning time-dependent PDE solver using message passing graph neural networks by Zhen Zhang, Brown University
  • June 24 Recording
  • June 24, 2022: From Neural SDEs to Neural SPDEs by Cristopher Salvi, Imperial College London
  • June 24, 2022: Learning Stochastic Dynamics with Statistics-Informed Neural Network by Yu-Hang Tang,  Lawrence Berkeley National Laboratory
  • June 17 Recording
  • June 17, 2022: Constitutive Artificial Neural Networks: a tailor-made tool for nonlinear anisotropic elasticity in biomechanics and beyond by Christian J. Cyron, Kevin Linka, Hamburg University of Technology
  • June 17, 2022: Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems by Minglang Yin, Brown University
  • June 10 Recording
  • June 10, 2022: Generative adversarial training of recurrent neural network for probabilistic forecast of random dynamical system by Kyongmin Yeo, IBM T.J. Watson Research Center
  • June 10, 2022: SVD Perspectives for Augmenting DeepONet: Flexibility and Interpretability by Simone Venturi & Tiernan Casey, Sandia National Laboratories
  • June 3 Recording
  • June 3, 2022: Molecular Contrastive Learning of Representation by Amir Barati Farimani, Carnegie Mellon University
  • June 3, 2022: Differential equations-related machine learning: revealing hidden dynamics and learning the solution operator of PDEs by Pipi Hu, Tsinghua University & Yanqi Lake Beijing Institute of Mathematical Sciences and Applications
  • May 27 Recording
  • May 27, 2022: Learning nonlinear dynamical systems from data using scientific machine learning by Romit Maulik, Argonne National Laboratory
  • May 27, 2022: Deep adaptive sampling for solving high-dimensional PDEs by Xiaoliang Wan, Louisiana State University
  • May 20 Recording
  • May 20, 2022: Multilevel PINNs (MPINNs) by Elisa Riccietti, Ecole Normale Superieure, Valentin Mercier, Serge Gratton, University of Toulouse
  • May 20, 2022: A new application to the universal operator approximation theorem of Deep Operator Networks, to model complex physical systems controlled by external processes by Mathieu Chalvidal, Brown University
  • May 12 Recording
  • May 12, 2022: Continuous Optimization for Learning Bayesian Networks by Yue Yu, Lehigh University
  • May 12, 2022: Learning-Based Actuator Placement and Allocation for Resilience against Attacks by Kyriakos Vamvoudakis, Georgia Institute of Technology
  • May 6 Recording
  • May 6, 2022: (Learned) simulation as the engine of physical scene understanding by Elias Cueto, Universidad de Zaragoza
  • May 6, 2022: Bi-fidelity Training of Neural Networks and Neural Operators by Alireza Doostan, Shubayan De, University of Colorado Boulder
  • May 6, 2022: Multifidelity Deep Operator Learning by Amanda Howard, Pacific Northwest National Laboratory
  • May 6, 2022: Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport by Lu Lu, University of Pennsylvania
  • April 29 Recording
  • April 29, 2022: Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations by Ben Moseley, University of Oxford
  • April 29, 2022: Scientific Machine Learning by Dr. Jeyan Thiyagalingam, Rutherford Appleton Laboratory, Science and Technology Facilities Council (STFC)
  • April 22 Recording
  • April 22, 2022: Neuroscience-Informed Neural Networks by Sindy Löwe, University of Amsterdam
  • April 22, 2022: Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction (Paper Review) by Aniruddha Bora, Brown University
  • April 15 Recording
  • April 15, 2022: Physics-informed Attention-based Neural Network for Solving Non-linear Partial Differential Equations  by Josep Ferre, Brown University
  • April 15, 2022:Neural Stochastic Partial Differential Equations Resolution-Invariant Learning of Continuous Spatiotemporal Dynamicsby Zongren Zou, Brown University
  • April 8 Recording
  • April 8, 2022: Physics-informed data based neural networks for two-dimensional turbulenceby Venkatesh Gopinath (Bosch Research and Technology Center) and Kannabiran Seshasayanan (Department of Physics, Indian Institute of Technology)
  • April 8, 2022: Graph representation learning in hyperbolic space by Sicheng Liu, Brown University
  • April 1 Recording
  • April 1, 2022: Some mathe-physical perspectives and effective theories on deep learning by Akshunna Shaurya Dogra, Imperial College London
  • April 1, 2022: Function regression using Spiking DeepONet  by Adar Kahana, Brown University
  • March 25 Recording
  • March 25, 2022: Physics-Informed Point Net A Deep Learning Solver for Steady-State Incompressible Flows and Thermal Fields on Multiple Sets of Irregular Geometries  by Ali Kashefi, Stanford University
  • March 25, 2022: Local approximation of operators by Hrushikesh Mhaskar, Claremont Graduate University
  • March 18 Recording
  • March 18, 2022: A New Framework for Solving Dynamical Systems by Xiu Yang, Lehigh University
  • March 18, 2022: Respecting Causality Is All You Need for Training Physics-informed Neural Networks by Varun Kumar, Brown University
  • March 11 Recording
  • March 11, 2022: Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks by Colby Wight, Pacific Northwest National Laboratory
  • March 11, 2022:Learning mappings from iced airfoils to aerodynamic coefficients using a deep operator networks by Vivek Oomen, Brown University
  • March 4 Recording
  • March 4, 2022: Physics-guided neural networks for constitutive modeling and nonlinear multiscale simulation by Oliver Weeger, Technical University of Darmstadt
  • March 4, 2022: Over-parameterization in manifold based surrogates and deep neural operators by Somdatta Goswami, Brown University
  • February 25 Recording
  • February 25, 2022: Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks by Bruno Loureiro, École Polytechnique Fédérale de Lausanne (EPFL)
  • February 25, 2022: Physics-guided neural networks for constitutive modeling and nonlinear multiscale simulation by Xueyu Zhu, University of Iowa
  • February 18 Recording
  • February 18, 2022: MaxwellNet Physics-driven deep neural network training based on Maxwell’s equations  by Demetri Psaltis, Ecole Polytechnique Federale de Lausanne
  • February 18, 2022: Improving Out-of-Distribution Robustness via Selective Augmentation (Review)  by Zongren Zou, Crunch Group
  • February 11 Recording
  • February 11, 2022: Learning Operators with Coupled Attention by Jacob and Georgios, University of Pennsylvania
  • February 4 Recording
  • February 4, 2022: A Physics-InformedA Physics-Informed Neural Network to model COVID-19 Infection and Hospitalization Scenarios Neural Network to model COVID-19 Infection and Hospitalization Scenarios by Sarah Treibert and Matthias Ehrhardt, Bergische Universität Wuppertal 
  • February 4, 2022:  Neural Networks with Inputs Based on Domain of Dependence and A Converging Sequence for Solving Conservation Laws by  Yingjie Liu, Georgia Institute of Technology
  • January 28 Recording
  • January 28, 2022: Transfer Learning on Multi-Fidelity Data
    by Daniel Tartakovsky, Stanford University
  • January 28, 2022: Unsupervised multimodal data-assimilation for AI-accelerated high-throughput experimentation by Nathaniel Albert Trask, Sandia National Laboratories
  • Janaury 28, 2022: Tutorial on Containers using by Khemraj Shukla, Brown University (Crunch members only)
  • January 21 Recording
  • January 21, 2022: Scientific Machine Learning by Umair Waheed, King Fahd University
  • January 21, 2022: Presentation will be given by Qian Zhang of Brown University
  • Janaury 14 Recording
  • January 14, 2022: Physics-informed machine learning using particle methods by Alessio Alexiadis, University of Birmingham
  • January 14, 2022: E(n) equivariant graph neural networks & E(n) equivariant graph normalizing flows by Zhen Zhang, Brown University
  • Janaury 7 Recording
  • January 7, 2022: Weisfeiler and Lehman Go Topological Message Passing Simplicial Networks by Yu Guang Wang, Shanghai Jiao Tong University
  • January 7, 2022: Hierarchical Learning to Solve Partial Differential Equations Using Physics-Informed Neural Networks  Networks by Yoonsang Lee,  Dartmouth College
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