
An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks
Solving inverse problems continues to be a challenge in a wide array of ...
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Integration of AI and mechanistic modeling in generative adversarial networks for stochastic inverse problems
The problem of finding distributions of input parameters for determinist...
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Solution of Physicsbased Bayesian Inverse Problems with Deep Generative Priors
Inverse problems are notoriously difficult to solve because they can hav...
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On the Reconstruction of Static and Dynamic Discrete Structures
We study inverse problems of reconstructing static and dynamic discrete ...
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Inverse design of twodimensional materials with invertible neural networks
The ability to readily design novel materials with chosen functional pro...
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Exascale Deep Learning for Scientific Inverse Problems
We introduce novel communication strategies in synchronous distributed D...
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A framework for datadriven solution and parameter estimation of PDEs using conditional generative adversarial networks
This work is the first to employ and adapt the imagetoimage translatio...
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A General Framework Combining Generative Adversarial Networks and Mixture Density Networks for Inverse Modeling in Microstructural Materials Design
Microstructural materials design is one of the most important applications of inverse modeling in materials science. Generally speaking, there are two broad modeling paradigms in scientific applications: forward and inverse. While the forward modeling estimates the observations based on known parameters, the inverse modeling attempts to infer the parameters given the observations. Inverse problems are usually more critical as well as difficult in scientific applications as they seek to explore the parameters that cannot be directly observed. Inverse problems are used extensively in various scientific fields, such as geophysics, healthcare and materials science. However, it is challenging to solve inverse problems, because they usually need to learn a onetomany nonlinear mapping, and also require significant computing time, especially for highdimensional parameter space. Further, inverse problems become even more difficult to solve when the dimension of input (i.e. observation) is much lower than that of output (i.e. parameters). In this work, we propose a framework consisting of generative adversarial networks and mixture density networks for inverse modeling, and it is evaluated on a materials science dataset for microstructural materials design. Compared with baseline methods, the results demonstrate that the proposed framework can overcome the abovementioned challenges and produce multiple promising solutions in an efficient manner.
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