Theory-guided Auto-Encoder for Surrogate Construction and Inverse
Modeling

Theory-guided Auto-Encoder for Surrogate Construction and Inverse Modeling

17 November 2020

Papers citing "Theory-guided Auto-Encoder for Surrogate Construction and Inverse Modeling"

9 / 9 papers shown

Title
Kolmogorov n-Widths for Multitask Physics-Informed Machine Learning (PIML) Methods: Towards Robust Metrics Michael Penwarden H. Owhadi Robert M. Kirby AI4CE 22 1 0 16 Feb 2024
MRF-PINN: A Multi-Receptive-Field convolutional physics-informed neural network for solving partial differential equations Shihong Zhang Chi Zhang Bo Wang AI4CE 11 3 0 06 Sep 2022
AutoKE: An automatic knowledge embedding framework for scientific machine learning Mengge Du Yuntian Chen Dongxiao Zhang AI4CE 23 11 0 11 May 2022
Use of Multifidelity Training Data and Transfer Learning for Efficient Construction of Subsurface Flow Surrogate Models Su Jiang L. Durlofsky AI4CE 13 29 0 23 Apr 2022
Deep reinforcement learning for optimal well control in subsurface systems with uncertain geology Y. Nasir L. Durlofsky OffRL AI4CE 9 16 0 24 Mar 2022
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next S. Cuomo Vincenzo Schiano Di Cola F. Giampaolo G. Rozza Maizar Raissi F. Piccialli PINN 18 1,162 0 14 Jan 2022
Deep-learning-based upscaling method for geologic models via theory-guided convolutional neural network Nanzhe Wang Q. Liao Haibin Chang Dongxiao Zhang AI4CE 18 5 0 31 Dec 2021
Uncertainty quantification and inverse modeling for subsurface flow in 3D heterogeneous formations using a theory-guided convolutional encoder-decoder network Rui Xu Dongxiao Zhang Nanzhe Wang AI4CE 28 17 0 14 Nov 2021
Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network Nanzhe Wang Haibin Chang Dongxiao Zhang 14 34 0 12 Oct 2021