Physics-enhanced deep surrogates for partial differential equations

Understand

Many physics and engineering applications demand Partial Differential Equations (PDE) property evaluations that are traditionally computed with resource-intensive high-fidelity numerical solvers.

• Data-driven surrogate models provide an efficient alternative but come with a significant cost of training.
• Emerging applications would benefit from surrogates with an improved accuracy-cost tradeoff, while studied at scale.
• Here we present a "physics-enhanced deep-surrogate" ("PEDS") approach towards developing fast surrogate models for complex physical systems, which is described by PDEs.