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Found 6 papers, 2 papers with code
Discretization Error of Fourier Neural Operators
Operator learning is a variant of machine learning that is designed to approximate maps between function spaces from data.
An operator learning perspective on parameter-to-observable maps
Computationally efficient surrogates for parametrized physical models play a crucial role in science and engineering.
Learning Homogenization for Elliptic Operators
However, a major challenge in data-driven learning approaches for this problem has remained unexplored: the impact of discontinuities and corner interfaces in the underlying material.
Learn Like The Pro: Norms from Theory to Size Neural Computation
The optimal design of neural networks is a critical problem in many applications.
Informative Neural Ensemble Kalman Learning
In stochastic systems, informative approaches select key measurement or decision variables that maximize information gain to enhance the efficacy of model-related inferences.
Neural Integration of Continuous Dynamics
Using the polynomial class of dynamical systems, we demonstrate the equivalence of neural and numerical integration.
