Adaptive hybrid coupling with operator inference, the overlapping Schwarz alternating method and reinforcement learning
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 each subdomain is fixed throughout a simulation. This is limiting for transient problems in which localized features propagate through the domain and the regions requiring high-fidelity resolution change over time. We introduce 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 boun...
| Authors | Trishit Mondal, Irina Tezaur, Anthony Gruber |
|---|---|
| Primary category | cs.LG |
| Published | 15 September 2026 |
| Last updated at source | 15 September 2026 |
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