Enforcing Imprecise Constraints on Generative Adversarial Networks for Emulating Physical Systems
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Publication:5163887
DOI10.4208/cicp.OA-2020-0106zbMath1490.76120arXiv1911.06671OpenAlexW3181354245MaRDI QIDQ5163887
Heng Xiao, Yang Zeng, Jin-Long Wu
Publication date: 9 November 2021
Published in: Communications in Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1911.06671
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Cites Work
- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- A consistent dual-mesh framework for hybrid LES/RANS modeling
- Constraint-aware neural networks for Riemann problems
- Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification
- DGM: a deep learning algorithm for solving partial differential equations
- Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks
- Enforcing constraints for interpolation and extrapolation in generative adversarial networks
- PDE-Net 2.0: learning PDEs from data with a numeric-symbolic hybrid deep network
- Stochastic seismic waveform inversion using generative adversarial networks as a geological prior
- MgNet: a unified framework of multigrid and convolutional neural network
- Inflow Turbulence Generation Methods
- Turbulent Flows
- A Comparison Study of Deep Galerkin Method and Deep Ritz Method for Elliptic Problems with Different Boundary Conditions
- Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations
- Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
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