Variational Parameter Calibration with Physics-Aware Latent-Space Surrogates
A physics-aware neural-network-based latent-space framework for reduced-order forward modeling and variational parameter estimation and the robustness of the proposed framework is demonstrated, which generally reduces calibration error and variability compared with the standard surrogate models.