A Theory-guided Auto-Encoder (TgAE) framework is proposed for surrogate construction and further used uncertainty quantification inverse modeling tasks. The built based on the (or Encoder-Decoder) architecture of convolutional neural network (CNN) via a theory-guided training process. In order to achieve training, governing equations studied problems can be discretized finite difference scheme ...

Traditionally, 1D models based on scaling laws have been used to parameterized convective heat transfer rocks in the interior of terrestrial planets like Earth, Mars, Mercury and Venus tackle computational bottleneck high-fidelity forward runs 2D or 3D. However, these are limited amount physics they can model (e.g. depth dependent material properties) predict only mean quantities such as mantle...

Abstract. This study presents a framework for rapid tsunami force predictions by the application of mode-decomposition-based surrogate modeling with 2D–3D coupled numerical simulations. A limited number large-scale analyses are performed selection scenarios variations in fault parameters to capture distribution tendencies target risk indicators. Then, proper orthogonal decomposition (POD) is ap...