2 citations · 7 across the 8 of their papers we have counts for
9 papers
Learning Long-Term Stable Operator Inference Reduced-Order Models of Fluid Flows through Online Spatial Filtering
Ian Moore, Ping-Hsuan Tsai, Anthony Gruber +4
This paper introduces an online evolve--filter--relax (EFR) strategy for long-term stability of Operator Inference (OpInf) reduced-order models (ROMs) of complex fluid flow simulat…
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…
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…
A Priori Error Bounds and Parameter Scalings for the Time Relaxation Reduced Order Model
Jorge Reyes, Ping-Hsuan Tsai, Julia Novo +1
The a priori error analysis of reduced order models (ROMs) for fluids is relatively scarce. In this paper, we take a step in this direction and conduct numerical analysis of the re…
A Time-Relaxation Reduced Order Model for the Turbulent Channel Flow
Ping-Hsuan Tsai, Paul Fischer, Traian Iliescu
Reg-ROMs are stabilization strategies that leverage spatial filtering to alleviate the spurious numerical oscillations generally displayed by the classical G-ROM in under-resolved…
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…
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,…