Physics-Constrained Neural Surrogate Models Domain Growth Under Conserved Kinetics

A revised arXiv preprint presents a neural surrogate model that incorporates physical constraints to predict how domains grow in systems governed by conserved kinetics. Such systems are typically described by nonlinear partial differential equations, which are costly to solve, and learned surrogates aim to offer faster alternatives. The work targets physics, chemistry, and biology applications where accurate spatiotemporal prediction matters.