A novel domain decomposition-based approach for creating adaptive hybrid models with the help of the Schwarz alternating method and some recent extensions of SAM are presented to enable the overlapping and non-overlapping coupling of non-intrusive polynomial and neural network-(NN-)based Operator Inference (OpInf) ROMs.
This work introduces a reinforcement learning (RL)-based approach for online adaptation of FOM-ROM models coupled via the overlapping Schwarz alternating method (O-SAM), an iterative domain decomposition method that solves subdomain-local problems while exchanging solution information through transmission boundary cond...
Trishit Mondal, Irina Tezaur, Anthony Gruber· 0 citations
A hybrid modeling framework for coupling pre-trained numerics-informed neural networks (NINNs) with classical full order models (FOMs) using the overlapping Schwarz alternating method, and the resulting hybrid NINN-FOM solutions agree closely with the corresponding FOM-FOM Schwarz solutions.
George Chumbipuma, Irina Tezaur, Alejandro N. Diaz et al.· 0 citations
Data-driven model order reduction methods such as operator inference enable the efficient construction of reduced-order models directly from high-dimensional time-domain data. Standard operator inference typically seeks a Galerkin-type reduced model in a prescribed low-dimensional subspace by identifying its reduced op...
J. Rettberg, Jonas Nicodemus, Harsh Sharma et al.· 0 citations
Across five well-established PDE benchmarks spanning steady-state prediction and time-dependent dynamics, Transolver$ achieves state-of-the-art with a benchmark-averaged relative error reduction of 33.4% over the strongest baseline for each metric, while consistently improving autoregressive rollout over single-operato...
Haonan Shangguan, Hang Zhou, Haixu Wu et al.· 0 citations
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 simulations that uses spatial filtering inspired from large eddy simulation to significantly improve long-term stability and predictive performance...
Ian Moore, Ping-Hsuan Tsai, Anthony Gruber et al.· 1 citation
This paper introduces a hybrid joint-selective optimization (HJSO) framework for large-scale numerical problems in which a small subset of trainable quantities is of primary interest. We partition the full parameter vector into a high-dimensional remaining block and a low-dimensional block of parameters of interest (PO...
M. Shahab, Gabriella Alfa Indahsari, I. Mukhlash et al.· 0 citations
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