Merging the classical and the modern in physics-based simulations

Abstract

Engineering simulations traditionally rely on finite element methods, which are accurate but computationally expensive, while scientific machine learning offers faster, data-driven alternatives. The recently developed neural-operator element method combines both approaches, making simulations more efficient and scalable.