Physics-informed semantic inpainting: Application to geostatistical modeling

19 September 2019

Papers citing "Physics-informed semantic inpainting: Application to geostatistical modeling"

8 / 8 papers shown

Title
Physics-Informed Computer Vision: A Review and Perspectives C. Banerjee Kien Nguyen Clinton Fookes G. Karniadakis PINN AI4CE 34 30 0 29 May 2023
Physical Knowledge Enhanced Deep Neural Network for Sea Surface Temperature Prediction Yuxin Meng Feng Gao Eric Rigall Ran Dong Junyu Dong Q. Du 29 20 0 19 Apr 2023
Magnetic Field Prediction Using Generative Adversarial Networks Stefan Pollok Nataniel Olden-Jorgensen P. S. Jørgensen Rasmus Bjørk GAN AI4CE 29 15 0 14 Mar 2022
Physics-Informed Deep Learning: A Promising Technique for System Reliability Assessment Taotao Zhou E. Droguett A. Mosleh AI4CE 11 25 0 24 Aug 2021
Inverse-Dirichlet Weighting Enables Reliable Training of Physics Informed Neural Networks S. Maddu D. Sturm Christian L. Müller I. Sbalzarini AI4CE 29 81 0 02 Jul 2021
Learning Functional Priors and Posteriors from Data and Physics Xuhui Meng Liu Yang Zhiping Mao J. Ferrandis George Karniadakis AI4CE 35 61 0 08 Jun 2021
Theory-guided Auto-Encoder for Surrogate Construction and Inverse Modeling Nanzhe Wang Haibin Chang Dongxiao Zhang AI4CE 39 49 0 17 Nov 2020
DiscretizationNet: A Machine-Learning based solver for Navier-Stokes Equations using Finite Volume Discretization Rishikesh Ranade C. Hill Jay Pathak AI4CE 54 123 0 17 May 2020