2 citations · 4 across the 11 of their papers we have counts for
5 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…
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…
Canonical and Noncanonical Hamiltonian Operator Inference
Anthony Gruber, Irina Tezaur
A method for the nonintrusive and structure-preserving model reduction of canonical and noncanonical Hamiltonian systems is presented. Based on the idea of operator inference, this…