• Corpus ID: 219559293

Inverse Estimation of Elastic Modulus Using Physics-Informed Generative Adversarial Networks

@article{Warner2020InverseEO,
  title={Inverse Estimation of Elastic Modulus Using Physics-Informed Generative Adversarial Networks},
  author={James E. Warner and Julian Cuevas and Geoffrey F. Bomarito and Patrick E. Leser and William P. Leser},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.05791}
}
While standard generative adversarial networks (GANs) rely solely on training data to learn unknown probability distributions, physics-informed GANs (PI-GANs) encode physical laws in the form of stochastic partial differential equations (PDEs) using auto differentiation. By relating observed data to unobserved quantities of interest through PDEs, PI-GANs allow for the estimation of underlying probability distributions without their direct measurement (i.e. inverse problems). The scalable nature… 
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References

SHOWING 1-10 OF 29 REFERENCES

Adversarial Uncertainty Quantification in Physics-Informed Neural Networks

Generative Adversarial Nets

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a…

Highly-Ccalable, Physics-Informed GANs for Learning Solutions of Stochastic PDEs

This work addresses the problem of modeling subsurface flow at the Hanford Site by combining stochastic computational models with observational data using physics-informed GAN models, and develops a highly optimized implementation that scales to 27,500 NVIDIA Volta GPUs.

Improved Training of Wasserstein GANs

This work proposes an alternative to clipping weights: penalize the norm of gradient of the critic with respect to its input, which performs better than standard WGAN and enables stable training of a wide variety of GAN architectures with almost no hyperparameter tuning.

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Adversarial Feature Matching for Text Generation

This work proposes a framework for generating realistic text via adversarial training, using a long short-term memory network as generator, and a convolutional network as discriminator, and proposes matching the high-dimensional latent feature distributions of real and synthetic sentences, via a kernelized discrepancy metric.

A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data

A rigorous convergence analysis is provided and exponential convergence of the “probability error” with respect to the number of Gauss points in each direction in the probability space is demonstrated, under some regularity assumptions on the random input data.

Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion

Automatic differentiation in machine learning: a survey

By precisely defining the main differentiation techniques and their interrelationships, this work aims to bring clarity to the usage of the terms “autodiff’, “automatic differentiation”, and “symbolic differentiation" as these are encountered more and more in machine learning settings.

Stochastic reduced order models for inverse problems under uncertainty.