Anton van Beek

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.