DDPS Webinar (in California time)

Join our engaging DDPS (Data-Driven Physical Simulations) seminars organized by the libROM team at Lawrence Livermore National Laboratory ( https://www.librom.net/ddps.html ). Held weekly—or occasionally bi-weekly on Thursdays or Fridays—these webinars spotlight how emerging machine learning and AI methods are transforming computational science and physical simulation. Our sessions cover cutting-edge topics such as deep learning for simulation, generative models, and innovative data assimilation techniques, alongside traditional modeling and advanced computational approaches applied to domains like fluid dynamics, plasma physics, and beyond. Whether you're interested in presenting a talk, recommending a speaker, or joining our vibrant DDPS community, please contact choi15@llnl.gov for further details or to be added to our email list.

Scheduled Talks in 2025

When	Speaker	Institution	Title	WebEx
May 2nd, 10 AM	Johannes Brandstetter	Johannes Kepler University Linz	The next wave of scientific breakthroughs is in the latent space	link
May 30th, 10:30 AM	Gábor Csányi	University of Cambridge	TBD	link
June 5th, 10 AM	Alessandro Alla	Universita di Roma	Data-Driven Algorithms for Online Identification and Control of Partial Differential Equations	link
Aug 15th, 10 AM	Jingwei Hu	University of Washington	TBD	link

Past Talks in 2024

Date	Speaker	Institution	Title	YouTube
Apr 18th	Sung Ha Kang	GeorgiaTech	Identifying differential equations from single observation with numerical methods: IDENT to WeakIDENT and more	link
Apr 11th	Cecilia Pagliantini	University of Pisa	Dynamical approximation and sensor placement for the state estimation of transport problems	link
Mar 28th	Kunihiko Taira	UCLA	Extreme Aerodynamics: Flow Analysis and Control for Highly Gusty Conditions	link
Feb 28th	Kyongmin Yeo	IBM	Reducing Data Resolution for better Reconstruction: Super-Resolution of Navier-Stokes Flows	link
Feb 20th	Youngjoon Hong	Seoul National University	Operator Networks Based on Numerical Analysis	link
Jan 31st	Soledad Le Clainche	Universidad Politecnica de Madrid	Hybrid reduced order models: from exploiting physical principles to novel machine learning approaches	link
Jan 24th	Max Welling	University of Amsterdam	Is AI Shifting the Paradigm of Scientific Discovery?	link
Dec 13th	Hamid Sadjadpour	UCSC	Everlasting Information-theoretic Encryption in the Era of Quantum Computing and AI	link
Nov 22nd	David Bortz	University of Colorado Boulder	The weak form is stronger than you think	link
Nov 15th	Yasaman Bahri	Google DeepMind	A first-principles approach to understanding deep learning	link
Nov 1st	Annalisa Quaini	University of Houston	Reducing the cost of ocean modeling with a data-driven ROM and LES	link
Oct 25th	Marius Zeinhofer	University Hospital Freiburg	Infinite Dimensional Optimization for Scientific Machine Learning	link
Oct 18th	Ching-Yao Lai	Stanford	Machine-Precision Neural Networks for Multiscale Dynamics	link
Sep 27th	Akhil Nekkanti	CalTech	Data-driven techniques for analysis of turbulent flows	link
Sep 20th	Jian-Xun Wang	University of Notre Dame	Neural Differentiable Physics: Unifying Numerical PDEs and Deep Learning for Data-Augmented Computational Physics	link
Aug 23rd	Aditi Krishnapriyan	UC Berkeley	Bringing numerical methods and deep learning with physics-constrained differentiable solvers	link
Aug 2nd	Elizabeth Qian	Georgia Tech	Multi-fidelity linear regression for scientific machine learning from scarce data	link
July 19th	Alberto Padovan	University of Illinois, Urbana-Champaign	Data-driven model reduction via non-intrusive optimization of projection operators and reduced-order dynamics	link
July 12th	Francesco Romor	Weierstrass Instititute	Intrusive model order reduction of parametric PDEs using neural networks approximants of the solution manifold and adaptive hyper-reduction	link
Jun 28th	Doug James	Stanford	Recent progress in reduced-order modeling for computer graphics and sound	link
Jun 21st	Gianluca Iaccarino	Stanford	AutoEncoders for Aerodynamic Predictions	link
Jun 14th	Fabio Giampaolo	University of Naples Federico II	Learning paradigms for neural networks: The locally backpropagated forward-forward algorithm	link
May 10th	Yexiang Xue	Purdue University	Vertical reasoning enhanced learning, generation and scientific discovery	link
Apr 12th	Burcu Beykal	University of Connecticut	A Data-Driven Process Systems Engineering Approach for Supply Chain Management and Enterprise-Wide Optimization	link
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