4 papers · 1 filter
In-context learning from self-generated trajectories for adaptive model reduction
Amirpasha Hedayat, Laura Balzano, Karthik Duraisamy
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…
History-aware adaptive reduced-order models via incremental singular value decomposition
Amirpasha Hedayat, Ali Mohaghegh, Laura Balzano +2
Reduced-order models (ROMs) can accelerate high-dimensional dynamical simulations, but their accuracy often deteriorates when online dynamics leave the regime represented by offlin…
Toward Adaptive Non-Intrusive Reduced-Order Models: Design and Challenges
Amirpasha Hedayat, Alberto Padovan, Karthik Duraisamy
Projection-based Reduced Order Models (ROMs) are often deployed as static surrogates, which limits their practical utility once a system leaves the training manifold. We formalize…
Attention-Enhanced Convolutional Autoencoder and Structured Delay Embeddings for Weather Prediction
Amirpasha Hedayat, Karthik Duraisamy
Weather prediction is a quintessential problem involving the forecasting of a complex, nonlinear, and chaotic high-dimensional dynamical system. This work introduces an efficient r…