Well-posed by Design: Learning Constitutive Laws from Velocity Data using Convex Neural Network Potentials

Learning constitutive laws of complex fluids from velocity data (i.e. indirect observations) is a PDE-constrained inverse problem in which expressive neural parameterizations risk breaking the well-posedness of the forward physics model. We address this tension by learning, rather than the constitut