PICL: Physics Informed Contrastive Learning for Partial Differential Equations

Neural operators have recently grown in popularity as Partial Differential Equation (PDEs) surrogate models. Learning solution functionals, rather than functions, has proven to be a powerful approach to calculate fast, accurate solutions to complex PDEs. While much work has been done evaluating neural operator performance on a wide variety of surrogate modeling tasks, these works normally evaluate performance on a single equation at a time. In this work, we develop a novel contrastive pretraining framework utilizing Generalized Contrastive Loss that …

cs.lg cs.na differential differential equation equation functions math.na operators performance physics physics.comp-ph solution solutions work