PICL: Physics Informed Contrastive Learning for Partial Differential Equations

Understand

Neural operators have recently grown in popularity as Partial Differential Equation (PDE) 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 improves neural operator generalization across multiple governing equations simultaneously.