• 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

Learning Parameters and Constitutive Relationships with Physics Informed Deep Neural Networks

A physics informed deep neural network method for estimating parameters and unknown physics (constitutive relationships) in partial differential equation (PDE) models and demonstrates that the proposed method is more accurate than state-of-the-art methods in the presence of measurement noise.

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

SRGAN, a generative adversarial network (GAN) for image super-resolution (SR), is presented, to its knowledge, the first framework capable of inferring photo-realistic natural images for 4x upscaling factors and a perceptual loss function which consists of an adversarial loss and a content loss.

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.

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.

Adam: A Method for Stochastic Optimization

This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.

MidiNet: A Convolutional Generative Adversarial Network for Symbolic-Domain Music Generation

This work proposes a novel conditional mechanism to exploit available prior knowledge, so that the model can generate melodies either from scratch, by following a chord sequence, or by conditioning on the melody of previous bars, making it a generative adversarial network (GAN).