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 2022

When Speaker Institution Title WebEx
June. 3, 10 AM Tailin Wu Stanford University Learning to accelerate large-scale physical simulations in fluid and plasma physics link
June. 23, 10 AM Molei Tao Georgia Institute of Technology TBD link
July. 1, 10 AM Dirk Hartmann Siemens TBD link
July. 28, 10 AM Ajay B. Harish University of Manchester Uncertainty quantification and deep learning for storm-surge prediction link
TBD Karen Veroy-Grepl Eindhoven University TBD link

Past Talks in 2022

Date Speaker Institution Title YouTube
May. 24 Michael Gleaves and Vassil Alexandrov Hartree Center Industrial engagement and research highlights at the Hartree Center 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 Princeton 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. 13, 10 AM 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 Brown U. 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