> ... Here we present a “physics enhanced deep-surrogate” (“PEDS”) approach towards developing fast surrogate models for complex physical systems, which is described by PDEs. Specifically, a combination of a low fidelity, explainable physics simulator and a neural network generator is proposed, which is trained end-to-end to globally match the output of an expensive high-fidelity numerical solver. Experiments on three exemplar test cases, diffusion, reaction–diffusion, and electromagnetic scattering models, show that a PEDS surrogate can be up to 3× more accurate
than an ensemble of feedforward neural networks with limited data (≈ 103 training points), and reduces the training data need by at least a factor of 100 to achieve a target error of
5%....
> ... Here we present a “physics enhanced deep-surrogate” (“PEDS”) approach towards developing fast surrogate models for complex physical systems, which is described by PDEs. Specifically, a combination of a low fidelity, explainable physics simulator and a neural network generator is proposed, which is trained end-to-end to globally match the output of an expensive high-fidelity numerical solver. Experiments on three exemplar test cases, diffusion, reaction–diffusion, and electromagnetic scattering models, show that a PEDS surrogate can be up to 3× more accurate than an ensemble of feedforward neural networks with limited data (≈ 103 training points), and reduces the training data need by at least a factor of 100 to achieve a target error of 5%....