2 citations · 4 across the 11 of their papers we have counts for
7 papers · 1 filter
Hybrid coupling with numerics-informed neural networks and the overlapping Schwarz alternating method
George Chumbipuma, Irina Tezaur, Alejandro Diaz +1
We develop a hybrid modeling framework for coupling pre-trained numerics-informed neural networks (NINNs) with classical full order models (FOMs) using the overlapping Schwarz alte…
Adaptive hybrid coupling with operator inference, the overlapping Schwarz alternating method and reinforcement learning
Trishit Mondal, Irina Tezaur, Anthony Gruber
Hybrid domain decomposition methods provide a flexible framework for coupling full order models (FOMs) and reduced order models (ROMs), but typically assume the model assigned to e…
Tackling Failure Modes of PINNs and PIKANs Using Conflict-Free Gradients
Sidharth S. Menon, Irina Tezaur, Ameya D. Jagtap
Scientific machine learning methods such as physics-informed neural networks (PINNs) increasingly rely on domain decomposition for better scalability while solving partial differen…
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…
Flexible and Stable Dynamics Discovery with Onsager's Variational Principle
Anthony Gruber, Ritoban Roy-Chowdhury, Irina Tezaur +1
Variational principles specify the dynamics of a physical system via the extremization of associated functional data. Onsager's variational principle (OVP), which characterizes dis…
On the role of relaxation and acceleration in the non-overlapping Schwarz alternating method for coupling
Giulia Sambataro, Irina Tezaur
The purpose of this paper is to study the influence of relaxation and acceleration techniques on the convergence behavior of the non-overlapping Schwarz algorithm with alternating…
NN-OpInf: an operator inference approach using structure-preserving composable neural networks
Eric Parish, Anthony Gruber, Patrick Blonigan +1
We propose neural network operator inference (NN-OpInf): a structure-preserving, composable, and minimally restrictive operator inference framework for the non-intrusive reduced-or…