PID-GAN: A GAN Framework based on a Physics-informed Discriminator for
Uncertainty Quantification with Physics

PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics

6 June 2021

Arka Daw

M. Maruf

Anuj Karpatne

Papers citing "PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics"

11 / 11 papers shown

Title
Generative Modeling of Random Fields from Limited Data via Constrained Latent Flow Matching James E. Warner Tristan A. Shah Patrick E. Leser Geoffrey F. Bomarito Joshua D. Pribe Michael C. Stanley AI4CE 29 0 0 19 May 2025
Physics-guided training of GAN to improve accuracy in airfoil design synthesis Kazunari Wada Katsuyuki Suzuki Kazuo Yonekura AI4CE 34 11 0 19 Aug 2023
PI-VEGAN: Physics Informed Variational Embedding Generative Adversarial Networks for Stochastic Differential Equations R. Gao Yufeng Wang Min Yang Chuanjun Chen GAN 34 2 0 21 Jul 2023
Generative Adversarial Reduced Order Modelling Dario Coscia N. Demo G. Rozza GAN AI4CE 67 5 0 25 May 2023
Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning P. Ai L. Xiao Z. Deng Yi Wang Xiangming Sun Guangming Huang Dong Wang Yulei Li Xinchi Ran 16 2 0 24 Apr 2023
Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook Xuan Di Rongye Shi Zhaobin Mo Yongjie Fu PINN AI4TS AI4CE 37 28 0 03 Mar 2023
Bayesian Physics-Informed Extreme Learning Machine for Forward and Inverse PDE Problems with Noisy Data Xu Liu Wenjuan Yao Wei Peng Weien Zhou PINN AI4CE 49 25 0 14 May 2022
PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations Weiheng Zhong Hadi Meidani DRL 30 37 0 21 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
Meta-Learning Dynamics Forecasting Using Task Inference Rui Wang Robin Walters Rose Yu OOD AI4TS AI4CE 31 32 0 20 Feb 2021
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 287 9,167 0 06 Jun 2015