4 papers
In-span learning: adapting reduced-order models using their own predictions
Amirpasha Hedayat, Laura Balzano, Karthik Duraisamy
Reduced-order models compress high-dimensional dynamics into low-dimensional representations that can be evaluated rapidly, but they lose accuracy when online dynamics drift beyond…
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…