3 papers
cs.LG2026
Scalable Gaussian process modeling of parametrized spatio-temporal fields
Srinath Dama, Prasanth B. Nair
We introduce a scalable Gaussian process (GP) framework with deep product kernels for data-driven learning of parametrized spatio-temporal fields over fixed or parameter-dependent…
cs.LG2026
Data-driven stochastic reduced-order modeling of parametrized dynamical systems
Andrew F. Ilersich, Kevin Course, Prasanth B. Nair
Modeling complex dynamical systems under varying conditions is computationally intensive, often rendering high-fidelity simulations intractable. Although reduced-order models (ROMs…
cs.LG2025
Learning Stochastic Multiscale Models
Andrew F. Ilersich, Prasanth B. Nair
The physical sciences are replete with dynamical systems that require the resolution of a wide range of length and time scales. This presents significant computational challenges s…