8 papers
Digital Twins in Coronary Artery Disease: A Mathematical Roadmap
Alessandro Veneziani, Annalisa Quaini, Marco Tezzele +2
The combination of data and models, enhanced by AI methodologies, leads to the paradigm called Digital Twins. This concept is expected to bring unprecedented support to personalize…
Hyperfastrl: Hypernetwork-based reinforcement learning for unified control of parametric chaotic PDEs
Anil Sapkota, Omer San
Spatiotemporal chaos in fluid systems exhibits severe parametric sensitivity, rendering classical adjoint-based optimal control intractable because each operating regime requires r…
SIMR-NO: A Spectrally-Informed Multi-Resolution Neural Operator for Turbulent Flow Super-Resolution
Muhammad Abid, Omer San
Reconstructing high-resolution turbulent flow fields from severely under-resolved observations is a fundamental inverse problem in computational fluid dynamics and scientific machi…
Stabilizing autoregressive forecasts in chaotic systems via multi-rate latent recurrence
Mrigank Dhingra, Omer San
Long-horizon autoregressive forecasting of chaotic dynamical systems remains challenging due to rapid error amplification and distribution shift: small one-step inaccuracies compou…
Method of Manufactured Learning for Solver-free Training of Neural Operators
Arth Sojitra, Omer San
Training neural operators to approximate mappings between infinite-dimensional function spaces often requires extensive datasets generated by either demanding experimental setups o…
Superresolving Non-linear PDE Dynamics with Reduced-Order Autodifferentiable Ensemble Kalman Filtering For Turbulence Modeling and Flow Regulation
Mrigank Dhingra, Omer San
Accurately reconstructing and forecasting high-resolution (HR) states from computationally cheap low-resolution (LR) observations is central to estimation-and-control of spatio-tem…