Mixture of Neural Operators: Incorporating Historical Information for Longer Rollouts
ICLR Workshop on AI4DifferentialEquations in Science, 2024
Traditional numerical solvers for time-dependent partial differential equations (PDEs) notoriously require high computational resources and necessitate recomputation when faced with new problem parameters. In recent years, neural surrogates have shown great potential to overcome these limitations. However, it has been …