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AI-Lorenz: A physics-data-driven framework for black-box and gray-box identification of chaotic systems with symbolic regression
Mario De Florio, Ioannis G. Kevrekidis, George Em Karniadakis
Discovering mathematical models that characterize the observed behavior of dynamical systems remains a major challenge, especially for systems in a chaotic regime. The challenge is…
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions
Michael Penwarden, Ameya D. Jagtap, Shandian Zhe +2
Physics-informed neural networks (PINNs) as a means of solving partial differential equations (PDE) have garnered much attention in the Computational Science and Engineering (CS&E)…
A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling
QiZhi He, Mauro Perego, Amanda A. Howard +2
One of the most challenging and consequential problems in climate modeling is to provide probabilistic projections of sea level rise. A large part of the uncertainty of sea level p…