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Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials

Yuwei Mao1, Zijiang Yang1, Dipendra Jha1

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This study introduces a new framework for inverse modeling in materials science, enabling efficient discovery of microstructures based on desired properties. The method uses generative adversarial networks and mixture density networks to overcome challenges in complex material structure-property relationships.

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Inverse Problems

Background:

  • Scientific applications utilize forward and inverse modeling paradigms.
  • Inverse problems are crucial for inferring unobservable causes from observed data, common in geophysics, healthcare, and materials science.
  • Discovering material microstructures from properties is a challenging inverse problem due to one-to-many nonlinear mappings and dimensional disparities.

Purpose of the Study:

  • To develop an efficient framework for inverse modeling of material structure-property linkages.
  • To address the challenges of microstructure discovery for a given target property.
  • To enable the identification of multiple potential microstructures corresponding to a specific material property.

Main Methods:

  • A novel framework combining generative adversarial networks (GANs) and mixture density networks (MDNs) was proposed.
  • The framework is designed for inverse modeling, specifically for microstructure discovery.
  • The approach tackles the one-to-many nonlinear mapping and dimensional challenges inherent in microstructure prediction.

Main Results:

  • The proposed framework successfully addresses the complexities of microstructure discovery.
  • It efficiently discovers multiple promising microstructural solutions for a given material property.
  • Performance is superior compared to existing baseline methods in solving inverse problems for materials.

Conclusions:

  • The developed framework offers an efficient and effective solution for inverse modeling in materials science.
  • It advances the ability to predict microstructures from material properties.
  • This work has significant implications for materials design and discovery.