DDPS Webinar (in California time)

DDPS stands for data-driven physical simulation. We hold weekly webinar, in average, either on Thursday or Friday at Lawrence Livermore National Laboratory. If you are interested in giving a webinar talk or would like to recommend a speaker, please send an email to choi15@llnl.gov. If you are interested in being included in DDPS email list, please also send an email to choi15@llnl.gov.

Scheduled Talks in 2024

When Speaker Institution Title WebEx
Apr 12th, 10 AM Burcu Beykal University of Connecticut A Data-Driven Process Systems Engineering Approach for Supply Chain Management and Enterprise-Wide Optimization link
Apr 26th, 10 AM Fabio Giampaolo University of Naples Federico II Learning paradigms for neural networks: The locally backpropagated forward-forward algorithm link
May 10th, 10 AM Yexiang Xue Purdue University Vertical reasoning enhanced learning, generation and scientific discovery link

Past Talks in 2024

Date Speaker Institution Title YouTube
Apr 5th Yanlai Chen UMass Dartmouth GPT-PINN and TGPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEs link
Feb 22nd Francesco Ballarin Università Cattolica del Sacro Cuore Regularized reduced order models for control of Navier--Stokes equations link
Feb 16th George Bollas The University of Connecticut Physics Informed Machine Learning through Symbolic Regression link
Feb 8th Zhiwen Zhang The University of Hong Kong DeepParticle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method link
Jan 26th Boris Kramer UCSD Structure-Preserving Learning of High-Dimensional Lagrangian and Hamiltonian Systems link
Jan 19th Jan Christoph UCSF Predicting Heart Rhythm Disorders from Spatio-Temporal Imaging Data using Artificial Intelligence link
Jan 12th Guang Lin Purdue University Towards Third Wave AI: Interpretable, Robust Trustworthy Machine Learning for Diverse Applications in Science and Engineering link

Past Talks in 2023

Date Speaker Institution Title YouTube
Dec 15th Dimitrios Giannakis Dartmouth College Quantum information and data science for modeling classical dynamics link
Dec 8th Nicolas Boulle The University of Cambridge Elliptic PDE learning is provably data-efficient link
Nov 17th Mengwu Guo University of Twente Probabilistic Methods for Data-Driven Reduced-Order Modeling link
Nov 3rd Michael E. Glinsky BNZ Energy Inc. A physics-based Reduced Order Model capturing the topology of dynamical manifolds link
Nov 3rd Elnaz Rezaian University of Michigan Data-driven balancing transformation for predictive model order reduction link
Oct 20th Christopher Rackauckas MIT Generalizing Scientific Machine Learning and Differentiable Simulation Beyond Continuous Models link
Oct 13th Jinlong Wu University of Wisconsin-Madison Data-Driven Closure Modeling Using Derivative-free Kalman Methods link
Sep 29th Krishna Garikipati University of Michigan Fokker-Planck-Inverse Reinforcement Learning: A physics-constrained approach to Markov Decision Process models with applications to cancer cell dynamics link
Sep 22nd Alice Cicirello University of Cambridge Challenges and opportunities for integrating physics-knowledge in machine learning strategies: friction identification case study link
Sep 8th Lu Lu Yale Deep neural operators with reliable extrapolation for multiphysics, multiscale & multifidelity problems link
Sep 1st Eleni Chatzi ETH Zürich Twinning and Model Discovery for Engineered Systems link
Aug 25th Khalid Jawed UCLA Reduced Order Modeling and Inverse Design of Flexible Structures by Machine Learning link
Aug 18th Amir Arzani University of Utah A flexible and generalizable XAI framework for scientific deep learning link
July 21st Thomas Beckers Vanderbilt University Physics-enhanced Gaussian Processes for Learning of Electromechanical Systems link
July 14th H.S. Udaykumar University of Iowa Data-driven multi-scale simulations for materials-by-design of energetic materials link
July 7th Alvaro Coutinho Federal University of Rio de Janeiro Enhancing data-driven workflows for complex simulations link
June 30th Peng Chen Georgia Institute of Technology Derivative-informed neural operators link
June 9th Dongbin Xiu The Ohio State University Data-driven Modeling of Unknown Systems with Deep Neural Networks link
May 26th Yexiang Xue Purdue University Scaling Up AI-driven Scientific Discovery via Embedding Physics Modeling into End-to-end Learning and Harnessing Random Projection link
May 19th Eduardo Gildin TAMU Guided Deep Learning Manifold Linearization of Porous Media Flow Equations for Digital Twins Operations link
May 12th Ying Liang Purdue University Data-assisted Algorithms for Inverse Random Source Scattering Problems link
May 5th Lori Brady Johns Hopkins University ML-driven Models for Material Microstructure and Mechanical Behavior link
May 4th Anima Anandkumar CalTech ML for Solving PDEs: Neural Operators on Function Spaces link
Apr. 27th Paul Atzberger UC Santa Barbara Generative Machine Learning Approaches for Data-Driven Modeling and Reductions of Non-Linear Dynamics in Scientific Simulation link
Apr. 14th Ameya Jagtap Brown University Scientific Machine Learning through the Lens of Physics-Informed Neural Networks link
Apr. 7th Matthias Chung Emory University Big Data Inverse Problems — Promoting Sparsity and Learning to Regularize link
Mar. 31st Jian Cao Northwestern University Physics-based AI-assisted Design and Control in Flexible Manufacturing link
Mar. 10th Hessam Babaee University of Pittsburgh CUR Matrix Decomposition for Scalable Reduced-Order Modeling of Nonlinear Partial Differential Equations using Time-Dependent Bases link
Feb. 24th Victor M. Zavala University of Wisconsin-Madison Bayesian Optimization: Exploiting Machine Learning Models, Physics, and High-Throughput Experiments link
Feb. 10th Soledad Villar Johns Hopkins University The Passive Symmetries of Machine Learning link
Jan. 27th Spencer H. Bryngelson GeorgiaTech Competitive Physics Informed Networks link
Jan. 20th Ajay B. Harish University of Manchester Uncertainty quantification and deep learning for storm-surge prediction link

Past Talks in 2022

Date Speaker Institution Title YouTube
Dec. 16th Ralph Smith North Carolina State University Parameter Subset Selection and Active Subspace Techniques for Engineering and Biological Models link
Nov. 17th Andrea Manzoni Politecnico di Milano Deep learning for reduced order modeling link
Nov. 11th Michael Brenner Harvard University Scientific Uses of Automatic Differentiation link
Nov. 4th, 12 PM Sorin Mitran University of North Carolina Data-driven information geometry approach to stochastic model reduction link
Oct. 21st Eric de Sturler Virginia Tech Cheap and robust adaptive reduced order models for nonlinear inversion and design link
Oct. 7th Jan Drgona PNNL Differentiable Programming for Modeling and Control of Dynamical Systems link
Sept. 16 Benjamin Sanderse CWI Amsterdam Structure-preserving learning of embedded, discrete closure models link
Aug. 25 Benjamin Erichson University of Pittsburgh Continuous Networks for Sequential Predictions link
Aug. 18 Santi Adavani RocketML Industrial Grade Scientific Machine Learning: Challenges and Opportunities link
July 21 Ricardo Vinuesa KTH Modeling and controlling turbulent flows through deep learning link
July 1 Dirk Hartmann Siemens Machine Learning and Physics-based Simulations – Yin and Yang of Industrial Digital Twins link
June 23 Molei Tao Georgia Institute of Technology Trustworthy learning of mechanical systems, and Stiefel optimization with applications to transformer and optimal transport link
June 3 Tailin Wu Stanford University Learning to accelerate large-scale physical simulations in fluid and plasma physics link
May 13 Ishan Khurjekar University of Florida Uncertainty-aware guided wave structural health monitoring using ensemble learning link
Apr. 28 Benjamin Peherstorfer New York University Neural Galerkin schemes with active learning for high-dimensional evolution equations link
Apr. 22 Petros Koumoutsakos Harvard University Artificial Intelligence and Scientific Computing for Fluid Mechanics link
Apr. 8 Daniel Floryan University of Houston Charting dynamics from data link
Mar. 25 Weinan E Princeton University Machine Learning and Multi-scale Modeling link
Mar. 17 Yannis Kevrekidis Johns Hopkins University No equations, no variables, no space, no time: Old and new results on data and the modeling of complex systems link
Mar. 11 Alice Cicirello TU Delft Interpretable, explainable and non-intrusive uncertainty propagation through expensive-to-evaluate models via ML-optimization link
Mar. 3 Ming Zhong TAMU Machine Learning of Self Organization from Observation link
Feb. 25 Lexing Ying Stanford Prony's method, analytic continuation, and quantum signal processing link
Feb. 18 Kaushik Bhattacharya Caltech Multi-scale modeling and neural operators link
Feb. 11 Sergei Tretiak LANL Machine Learning for materials and chemical dynamics link
Feb. 4 Serkan Gugercin Virginia Tech Data-driven modeling of dynamical systems: A systems theoretic perspective link
Jan. 28 Ashesh Chattopadhyay Rice University Deep learning meets data assimilation: On physically-consistent architectures and hybrid ensemble Kalman filters for weather forecasting link
Jan. 20 Pat Langley Stanford University Computational Scientific Discovery: Heuristic Search for Communicable Laws and Models link
Jan. 14 Greg Beroza Stanford University Towards Complete Machine-Learning-Based Earthquake Monitoring Workflows link
Jan. 6 Miles Cranmer Princeton University The Problem with Deep Learning in Physics (and how to fix it) link

Past Talks in 2021

Date Speaker Institution Title YouTube
Dec. 3rd Igor Mezic UC Santa Barbara Koopman operator theory for dynamical systems, control and data analytics link
Nov. 18th Michael Mahoney UC Berkeley Toward combining principled scientific models and principled machine learning models link
Nov. 12th WaiChing Sun Columbia University Data-driven constitutive updates: from model-free poroelasticity to level set plasticity trained by neural networks link
Nov. 4th Misha Khodak CMU Towards automatic architecture design for emerging machine learning tasks link
Oct. 28th Masayuki Yano U. of Toronto Towards reliable, efficient, and automated model reduction of parametrized nonlinear PDEs: error estimation, adaptivity, and application to aerodynamics link
Oct. 7th Youngsoo Choi LLNL libROM: Library for physics-constrained data-driven physical simulations link
Sep. 30th Peter Benner Max Planck Identification of Nonlinear Dynamical Systems from Noisy Measurements link
Sep. 2nd Ionut-Gabriel Farcas Oden Institute Learning hierarchies of reduced-dimension and context-aware low-fidelity models for multi-fidelity Monte Carlo sampling link
Aug. 26th Jesse Capecelatro U. of Michagan Turbulent disperse two-phase flows: simulations and data-driven modeling link
Aug. 19th Christopher J. Earls Cornell U. Gaining mechanistic insight through learning Green's functions: Uncovering the solutions to hidden PDEs link
Aug. 13th Dmitriy Anistratov North Carolina State U. Reduced order models for thermal radiative transfer problems based on moment equations and POD/DMD of Eddington tensor link
Aug. 5th Luca Magri Imperial College, London Physics-aware reservoir-computing for turbulence and chaotic learning NA
Jul. 30th Marta D'Elia Sandia Data-driven learning of nonlocal models: bridging scales and design of new neural networks link
Jul. 22nd Hannah Lu Stanford U. Dynamic model decomposition for reduced order modeling in flow and transport problems NA
Jul. 15th Yeonjong Shin KAIST Towards a mathematical understanding of modern machine learning: theory, algorithms, and applications link
Jul. 9th Rui Wang UC, San Diego Physics-guided deep learning for dynamics for forecasting link
Jul. 1st Tan Bui UT, Austin Model-constrained deep learning approaches for inference, control, and uncertainty quantification link
Jun. 24th Matthew Zahr U. of Notre Dame Model reduction of convection-dominated partial differential equations via optimization-based implicit feature tracking link
Jun. 10th David Ryckelynck Mines ParisTech Model order reduction assisted by deep neural networks (ROM-net) link
Jun. 3rd Harbir Antil George Mason U. Applications of Fractional Operators from Optimal Control to Machine Learning link
May 27th Romit Maulik Argonne Neural architecture search for surrogate modeling link
May 20th Tobias Pfaff, Alvaro Sanchez-Gonzalez DeepMind Learning physical simulation with Graph Network link
May 14th George Karniadakis Brown U. Approximating functions, functionals, and operators using deep neural networks for diverse applications link
Apr. 29th Traian Iliescu Virginia Tech. Large Eddy Simulation Reduced Order Models (LES-ROMs) link
Apr. 15th Tommaso Taddei Inria Registration-based model reduction of parameterized advection-dominated PDEs link
Apr. 7th Francisco Chinesta ENSAM ParisTech Empowering Hybrid Twins from Physics-Informed Artificial Intelligence link
Apr. 1st Priya Donti Carnegie Mellon U. Incorporating power system physics into deep learning via implicit layers link
Mar. 25th Mario Ohlberger U. Munster Model reduction with adaptive enrichment for large scale PDE-constrained optimization link
Mar. 18th Karthik Duraisamy U. of Michigan Towards Robust, Accurate and Tractable Reduced Order Models for Multi-scale, Multi-physics Problems link
Mar. 10th Pawan Goyal Max Planck Physics-informed learning for nonlinear dynamical systems: a deep learning approach to operator inference link
Feb. 18th Nils Thuerey Technische Universitat Munchen Differentiable Physics Simulations for Deep Learning link
Jan. 27th Alfio Quarteroni EPFL The mathematical heart: a computational model for the simulation of the heart function link
Jan. 21th Steven Brunton U. of Washington Interpretable and Generalizable Machine Learning for Fluid Mechanics link
Jan. 7th Irina Tezaur Sandia The Schwarz alternating methods as a means for concurrent multiscale coupling in solid mechanics link

Past Talks in 2020

Date Speaker Institution Title YouTube
Dec. 17th Jan Hesthaven EPFL Non-intrusive reduced order models using physics informed neural networks link
Dec. 10th Jesse Chan Rice U. Entropy stable schemes for nonlinear conservation laws: high order discontinuous Galerkin methods and reduced order modeling link
Nov. 18th Paris Perdikaris UPenn When and why physics-informed neural networks fail to train: A neural tangent kernel perspective link
Nov. 12th Donsub Rim Courant Institute Distilling nonlinear shock waves: Nonlinear model reduction for transport dominated problems using deep neural networks link
Oct. 29th Byungsoo Kim ETH Zurich Data-Driven Methods for Fluid Simulations in Computer Graphics link
Oct. 15th Youngkyu Kim UC Berkeley A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder link