In-context learning from self-generated trajectories for adaptive model reduction
High-fidelity simulations of complex physical systems are often too expensive for repeated prediction, design, and control. Reduced-order models address this computational cost by compressing high-dimensional dynamics into low-dimensional representations that can be evaluated rapidly, but they often lose accuracy when online dynamics drift beyond the offline training data. Adaptive methods address this limitation by updating the reduced subspace online using external, out-of-span information, such as full-order corrections or sensor snapshots. We discovered that a complementary and previously unexploited in-span adaptation channel exists within the current reduced subspace. To exploit thi...
| Authors | Amirpasha Hedayat, Laura Balzano, Karthik Duraisamy |
|---|---|
| Primary category | cs.LG |
| Published | 3 July 2026 |
| Last updated at source | 20 September 2026 |
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