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 2023
| When | Speaker | Institution | Title | WebEx |
|---|---|---|---|---|
| Mar. 31st, 10 AM | Jian Cao | Northwestern University | Physics-based AI-assisted Design and Control in Flexible Manufacturing | link |
| Apr. 7th, 10 AM | Matthias Chung | Emory University | Big Data Inverse Problems — Promoting Sparsity and Learning to Regularize | link |
| Apr. 14th, 10 AM | Ameya Jagtap | Brown University | TBD | link |
| May 4th, 10 AM | Anima Anandkumar | CalTech | TBD | link |
| May 5th, 10 AM | Lori Brady | Johns Hopkins University | ML-driven Models for Material Microstructure and Mechanical Behavior | link |
| May 12th, 10 AM | Ying Liang | Purdue University | Deep generative models for Inverse Random Source Scattering Problems | link |
| May 19th, 10 AM | Eduardo Gildin | TAMU | TBD | link |
| TBD | Karen Veroy-Grepl | Eindhoven University | TBD | link |
| TBD | Annalisa Quaini | University of Houston | TBD | link |
Past Talks in 2023
| Date | Speaker | Institution | Title | YouTube |
|---|---|---|---|---|
| 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 |