14 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…
Evolutionary Feature Engineering for Structured Data
Ege Onur Taga, Yilin Zhuang, M. Emrullah Ildiz +4
Large language models are increasingly used as open-ended search operators in evolutionary optimization. We introduce Evolutionary Feature Engineering (EFE), a framework for using…
Predictivity and Utility of Neural Surrogates of Multiscale PDEs
Karthik Duraisamy
Scientific machine learning is increasingly being spoken of as universal emulators for classical numerical solvers for multi-scale partial differential equations, but most apparent…
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…
Two-point Turbulence Closures in Physical Space
Noah Zambrano, Karthik Duraisamy
This work presents a predictive two-point statistical closure framework for turbulence formulated in physical space. A closure model for ensemble-averaged, incompressible homogeneo…