Learning the Geometry of Admissible Hypotheses through Inductive Bias in Training Distributions
This work presents a framework for learning continuous latent representations of admissible partial differential equations by embedding a scientific inductive bias directly into the training distribution, and shows that embedding a scientific inductive bias in the training distribution enables the learning of compact and geometrically meaningful hypothesis manifolds.
James Crowley, Faez Ahmed, A. van Beek
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