Designing accurate emulators for scientific processes using calibration-driven deep models
Designing accurate emulators for scientific processes using calibration-driven deep models
Abstract Predictive models that accurately emulate complex scientific processes can achieve speed-ups over numerical simulators or experiments and at the same time provide surrogates for improving the subsequent analysis. Consequently, there is a recent surge in utilizing modern machine learning methods to build data-driven emulators. In this work, we study …