Machine Learning + X Seminars 2021

December 17 Recording
December 17, 2021: Confluence of numerical modeling methods and artificial intellingence in physics-based simulations by Seid Koric and Diab W. Abueidda, National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign
December 17, 2021: Numerical Solution of Partial Differential Equations and initial value problems of stiff ODEs with physics informed raandom projection Neural Networks by Constantinos Siettos, University of Naples
December 10 Recording
December 10, 2021: Machine learning for numerical PDE fast rate, neural scaling law by Yiping Lu, Stanford University
December 10, 2021: Learning Laws of datasets by
João M Pereira, University of Texas
December 3 Recording
December 3, 2021: Accurate and Efficient Physics-Informed Learning Through Differentiable Simulation by Chris Rackauckas, MIT
December 3, 2021: One-Shot Transfer Learning of Physics-Informed Neural Networks by Marios Matthaiakis, Harvard
November 26 Recording
November 26, 2021: Multi-Objective Loss Balancing for Physics-Informed Deep Learningby Kraus Michael Anton and Rafael Bischof, ETH Zurich
November 26, 2021: Physics-informed neural networks for 1D sound field predictions with parameterized sources and impedance boundaries by Nikolas Borrel-Jensen, Technical University of Denmark
November 19 Recording
November 19, 2021: Utilizing data and physical constraints in machine learning-enabled computational solid mechanics and multiphysics by Nikolaos Bouklas, Cornell University
November 19, 2021: A Neural Network Ensemble Approach to System Identification by Elisa Negrini, WPI
November 12 Recording
November 12, 2021: DeepBND a Machine Learning approach to enhance Multiscale Solid Mechanics by
Felipe Rocha, Ecole polytechnique fédérale de Lausanne
November 12, 2021: How Machine Learning can help in earthquake control and fault mechanics by Ioannis Stefanou and Filippo Masi, Ecole Centrale de Nantes (ECN)
November 5 Recording
November 5, 2021: Deep Learning for the Closure and Solution of Partial Differential Equations by Justin Sirignano, Associate Professor, Mathematics, University of Oxford
November 5, 2021: Local Extreme Learning Machines (locELM) A Neural Network-Based Spectral Element-Like Method for Solving PDEs by Suchuan (Steven) Dong, Professor, Center for Computational and Applied Mathematics, Department of Mathematics, Purdue University
October 29 Recording
October 29, 2021: Predicting Tropical Cyclone Intensity Using Deep Learning by Wenwei Xu, PNNL
October 29, 2021: Characterizing possible failure modes in physics-informed neural networks by Aditi Krishnapriyan, UC
Berkeley
October 22 Recording
October 22, 2021: Domain Adaptation Methods for Deep Regression by Xinyang Chen, Tsinghua University
October 22, 2021: Data-driven computation for invariant probability measures by Yao Li, UMass Amherst
October 15 Recording
October 15, 2021: Enabling Rapid Meshless Multiphysics With Hybrid Data-Driven Projection-Tree Reduced-Order Modeling by Steven Rodriguez, U.S. Naval Research Laboratory
October 15, 2021: Optimal renormalization of multiscale systems by Panos Stinis, PNNL
October 8 Recording
October 8, 2021: Galerkin Transformer by Shuhao Cao, WUSTL
October 8, 2021: Operator Networks with Predictive Uncertainty for Partial Differential Equations with Inhomogeneous Boundary Conditions by Nickolas D Winovich, Purdue
October 1 Recording
October 1, 2021: GrADE: A graph-based data-driven solver for time-dependent nonlinear partial differential equations by Souvik Chakraborty, IIT Delhi
October 1, 2021: A spectral approach for time-dependent PDEs using machine-learned basis functions by Saad Qadeer, Pacific Northwest National Laboratory and Brek Meuris, University of Washington
September 24 Recording
September 24, 2021: Thoughts on Deep Learning by Ron DeVore, Texas A&M University
September 24, 2021: Whirlwood tour of linearization and beyond in deep learning by Jason Lee, Princeton University
September 17 Recording
September 17, 2021: Neural Nets and Numerical PDEs by Zhiqiang Cai, Purdue University
September 17, 2021: GFINNs: GENERIC Formalism Informed Neural Networks for Deterministic and Stochastic Dynamical Systems by Zhen Zhang, CRUNCH Group
September 10 Recording
September 10, 2021: Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems by Jeremy Yu, St. Mark’s School of Texas
September 10, 2021: Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models by Katiana Kontolati, Johns Hopkins University
September 3 Recording
September 3, 2021: Systems biology informed deep learning applied to a red blood cell clearance model model by Achilles Gatsonis, Brown University
September 3, 2021: High order finite element methods with extra smoothness by Charles Parker, Brown University
August 27 Recording
August 27, 2021: Deep Learning for Turbulent Flows and Physics-Informed-Neural-Networks (PINNs) Applications by Dr. Ricardo Vinuesa & Hamidreza Eivazi, KTH Royal Institute of Technology Applications by Dr. Ricardo Vinuesa & Hamidreza Eivazi, KTH Royal Institute of Technology
August 27, 2021: (Paper Review PhyCRNet Physics-informed Convolutional-Recurrent Network for Solving Spatiotemporal PDEsby Varun Kumar
August 20 Recording
August 20, 2021: A Crash Course on Reinforcement Learning by Kamaljyoti Nath, Brown University
August 20, 2021: Data-driven discovery of governing equations for fluid dynamics based on molecular simulation by Xuhui Meng, Brown University
August 13 Recording
August 13, 2021: PDE-Constrained Models with Neural Network Terms Optimization and Global Convergence by Prof. Konstantinos Spiliopoulos, Boston University
August 13, 2021: Machine learning for numerical PDE fast rate, neural scaling lawby Prof. Grace Gu, UC Berkeley
August 6 Recording
August 6, 2021: Data-driven modeling for unsteady aerodynamics and aeroelasticity by Jiaqing Kou, Technical University of Madrid
August 6, 2021: Explore missing flow dynamics by physics-informed deep learning the parametrised governing system by Patricio Clark Di Leoni
July 30 Recording
July 30, 2021: Machine Learning Approaches for Thermodynamic and Kinetic Analysis of Molecular Systems by Hao Wu, Tongji University, China
July 30, 2021: Nonlocal Kernel Network (NKN) a Stable and Resolution-Independent Deep Neural Network by Yue Yu, Lehigh University
July 23 Recording
July 23, 2021: Partition of Unity Networks for Deterministic and Probabilistic Regression by Mamikon Gulian, Sandia National Laboratories
July 23, 2021: Neural Network Method for solving parabolic two-temperature microscale heat conduction in double-layered thin films exposed to ultrashort-pulsed lasers by Aniruddha Bora, Louisiana tech University
July 16 Recording
July 16, 2021: Paper Review – Simulating Continuum Mechanics with Multi-Scale Graph Neural Networks by Presented by Shengze Cai, CRUNCH Group
July 16, 2021: Paper Review – Machine Learning the Kinematics of Spherical Particles in Fluid Flow by Leo Liu, CRUNCH Group
July 9 Recording
July 9, 2021: Researches on Molecular Geometry and Related Applications by Sheng Gui, Chinese Academy of Sciences
July 2 Recording
July 2, 2021:Artificial Intelligence for Engineering Design and Computational Mechanics by Ramin Bostanabad, University of California, Irvine
July 2, 2021: Learning Functional Priors and Posteriors from Data and Physics by Xuhui Meng, CRUNCH Group
June 25 Recording
June 25, 2021: Transfer learning based multi-fidelity physics informed deep neural network by Souvik Chakraborty, Indian Institute of Technology
June 25, 2021: On optimization and generalization in deep neural networks by Kenji Kawaguchi, Harvard University
June 18 Recording
June 18, 2021: Recent Developments in the Mathematics of Neural Nets by Anirbit Mukherjee, University of Pennsylvania
June 18, 2021: Predicting High-Stress Regions Inside Microstructure Using Deep Learning by Ankit Shrivastava, Carnegie Mellon University
June 11 Recording
June 11, 2021: Physics Embedded Machine Learning Methods in Hierarchical Constitutive And Damage Modeling of Metals and Composites by Somnath Ghosh, Johns Hopkins University
June 11, 2021: Can stable and accurate neural networks be computed? On the barriers of deep learning and Smale’s 18th problem by Matthew Colbrook, University of Cambridge
June 4 Recording
June 4, 2021: Data-Driven Methods of Accelerating Physical Simulations and Their Applications by Youngsoo Choi, Lawrence Livermore National Laboratory
June 4, 2021: Physics-infused Machine Learning and Evolutionary Learning Approaches to Design Intelligent Robotic Systems by Amir Behjat, University at Buffalo
May 28 Recording
May 28, 2021: Parametric deep energy approach for elasticity accounting strain gradient effects (Paper review) by
Somdatta Goswami, Crunch Group
May 28, 2021: Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systems (Paper review) by Liu Yang, Crunch Group
May 21 Recording
May 21, 2021: Self-Adaptive Physically-Informed Neural Networks by Ulisses M. Braga-Neto, Texas A&M University
May 21, 2021: MorphNet Fast and Simple Resource-Constrained Structure Learning of Deep Networks Presented by Zongren Zou, CRUNCH Group
May 14 Recording
May 14, 2021: Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks by N. Sukumara/Ankit Srivastava, Illinois Institute of Technology
May 14, 2021: Physics-informed Learning for Data-driven Discovery of Governing Laws by Hao Sun, Northeatern University
May 7 Recording
May 7, 2021:PDE-Constrained Models with Neural Network Terms Optimization and Global Convergence by Luca Magri, Imperial College London
May 7, 2021: Hierarchical Multi-Instance Learning Theory, Some Applications, and Benefits by Tomas Pevny, Czech Technical University in Prague
April 30 Recording
April 30, 2021: Physics-Informed Neural Networks for Elliptic Partial Differential Equations on 3D Manifolds by Cem Celik, Brown University
April 30, 2021: A Caputo fractional derivative-based algorithm for optimization by Yeonjong Shin, Brown University
April 23 Recording
April 23, 2021: Solvin Solving and Learning Nonlinear PDEs with Gaussian Processes g and Learning Nonlinear PDEs with Gaussian Processes by Bamdad Hosseini, Caltech
April 23, 2021: A Machine-Learning Method for Time-Dependent Wave Equations over Unbounded Domains by Ameya Jagtap, Brown University
April 16 Recording
April 16, 2021: Distributional Offline Continuous-Time Reinforcement Learning with Neural Physics-Informed PDEs by Igor Halperin, AI AM Research, Fidelity Investments
April 16, 2021:Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets by Somdatta Goswami, Brown University
April 9 Recording
April 9, 2021: Accelerating kinetic simulations of magnetic-confinement fusion devices utilizing encoder-decoder neural networks to numerically solve the nonlinear Fokker-Planck collision operator by Michael Churchill, Princeton Plasma Physics Laboratory
April 9, 2021: A New Stochastic Dynamic Graph Embedding Model – DynG2G by Apoorva Vikram Singh
April 2 Recording
April 2, 2021: Hyper-differential sensitivity analysis for control under uncertainty of aerospace vehicles by Bart van Bloemen Waanders, Sandia National Laboratory
April 2, 2021: Incorporating Physical Principles into Deep Dynamics Models by Rose Yu, Assistant Professor, Computer Science and Engineering, UC San Diego
March 26 Recording brown.edu/…loads/Flyer_4.2.21_Rose Yu.pdf
March 26, 2021: Learning emergent PDEs in a learned emergent space by Prof. Ioannis G. Kevrekidis, Johns Hopkins University
March 26, 2021: Dissertation Defense – Generative Adversarial Networks for Physics-Informed Learning by Liu Yang
March 19 Recording
March 19, 2021: Convergence Analysis of Numerical PDEs by Neural Network Functions by Prof. Jinchao Xu, The Pennsylvania State University
March 19, 2021: A Simple Modeling Framework For Prediction In The Human Glucose-Insulin System by Zhen Zhang, CRUNCH
March 12 Recording
March 12, 2021: Orbital Dynamics of Binary Black Hole Systems can be Learned from Gravitational Wave Measurements by Brendan Keith
March 12, 2021: Local error quantification for Neural Network Differential Equation solvers by Beichuan Deng, Worcester Polytechnic Institute
March 5 Recording
March 5, 2021: Deep neural network surrogates for non-smooth quantities of interest in shape uncertainty quantification by Laura Scarabosio, Radboud University, The Netherlands
March 5, 2021: SIAG CSE Early Career Prize Lecture Bridging Physical Models and Observational Data with Physics-Informed Deep Learning by Paris Perdikaris, University of Pennsylvania, U.S.
March 5, 2021: SIAM-ACM Prize in Computational Science and Engineering Lecture DeepOnet Learning Linear Nonlinear and Multiscale Operators Using Deep Neural Networks Based on the University Approximation Theorem of Operators by George E. Karniadakis, Brown University, U.S.
February 26 Recording
February 26, 2021: Theoretical Guarantees of Machine Learning Methods for Solving High Dimensional PDEs by Yulong Lu , University of Massachusetts, Amherst
February 26, 2021: Sparsity in Deep Learning Pruning and growth for efficient inference and training in neural networks by Zongren, Zou CRUNCH
February 19 Recording
February 19, 2021: Optimization and Learning With Nonlocal Calculus by Sriram Nagaraj, Quantitative Specialist at Federal Reserve Bank of Atlanta
Fecruary 19, 2021: Learning Poisson systems and trajectories of autonomous systems via Poisson neural networks by Zhen Zhang, CRUNCH
February 12 Recording
February 12, 2021: On the eigenvector bias of Fourier feature networks From regression to solving multi-scale PDEs with physics-informed neural networks by Sifan Wang
February 12, 2021:Physics-informed neural networks with hard constraints for inverse design by Lu Lu
February 5 Recording
February 5, 2021: Deep evidential classification regression by Apostolos Psaros
February 5, 2021: FBSDE based neural network algorithms for high-dimensional quaslinear parabolic PDEs by Wenzhong Zhang, Southern Methodist University
Janaury 29 Recording
Janaury 29, 2021: Deep reconstruction of strange attractors from time by Ehsan Kharazmi
January 29, 2021: Integrating Machine Learning & Multiscale Modeling in Biomedicine by Lu Lu, Department of Mathematics, Massachusetts Institute of Technology
Janaury 22 Recording
January 22, 2021: Learning Local Conservation Laws via Inversion by Jong-Hoon Ahn
January 22, 2021: Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting by Zhen Zhang
Janaury 22, 2021: Adversarial Sparse Transformer for Time Series Forecasting by Jeremy Chen
January 15 Recording
January 15, 2021: nnU-Net a self-configuring method for deep learning-based biomedical image segmentation by Mengjia Xu
January 15, 2021: Towards a mathematical understanding of modern machine learning theory and algorithm by Yeonjong Shin
January 8 Recording
January 8, 2021:Control volume PINN a method for solving inverse problems with hyperbolic PDEs by Patel, Ravi Ghanshyam, Sandia national laboratories
January 8, 2021: Gaussian Processes Kernels and Neural Tangent Kernel of Deep Neural Networks by Jiaxi Zhao, Stony Brook University