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 |