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Found 2 papers, 1 papers with code
Solving PDEs on Unknown Manifolds with Machine Learning
In a well-posed elliptic PDE setting, when the hypothesis space consists of neural networks with either infinite width or depth, we show that the global minimizer of the empirical loss function is a consistent solution in the limit of large training data.
Machine Learning for Prediction with Missing Dynamics
This article presents a general framework for recovering missing dynamical systems using available data and machine learning techniques.
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