7 papers
StabOp: A Data-Driven Stabilization Operator for Reduced Order Modeling
Ping-Hsuan Tsai, Anna Ivagnes, Annalisa Quaini +2
Spatial filters have played a central role in large eddy simulation and, more recently, in reduced order model (ROM) stabilization for convection-dominated flows. Nevertheless, imp…
Accelerating Galerkin Reduced-Order Models for Turbulent Flows with Tensor Decomposition
Ping-Hsuan Tsai, Paul Fischer, Edgar Solomonik
Galerkin-based reduced-order models (G-ROMs) offer efficient and accurate approximations for laminar flows but require hundreds to thousands of modes to capture the complex dyn…
Defining Foundation Models for Computational Science: A Call for Clarity and Rigor
Youngsoo Choi, Siu Wun Cheung, Youngkyu Kim +9
The widespread success of foundation models in natural language processing and computer vision has inspired researchers to extend the concept to scientific machine learning and com…
Verifiability and Limit Consistency of Eddy Viscosity Large Eddy Simulation Reduced Order Models
Jorge Reyes, Ping-Hsuan Tsai, Ian Moore +2
Large eddy simulation reduced order models (LES-ROMs) are ROMs that leverage LES ideas (e.g., filtering and closure modeling) to construct accurate and efficient ROMs for convectio…
Symbolic Regression of Data-Driven Reduced Order Model Closures for Under-Resolved, Convection-Dominated Flows
Simone Manti, Ping-Hsuan Tsai, Alessandro Lucantonio +1
Data-driven closures correct the standard reduced order models (ROMs) to increase their accuracy in under-resolved, convection-dominated flows. There are two types of data-driven R…
Local Reduced-Order Modeling for Electrostatic Plasmas by Physics-Informed Solution Manifold Decomposition
Ping-Hsuan Tsai, Seung Whan Chung, Debojyoti Ghosh +3
Despite advancements in high-performance computing and modern numerical algorithms, computational cost remains prohibitive for multi-query kinetic plasma simulations. In this work,…