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June 27, 2026MetalsOpen Access

CPGAN: A Multi-Input Conditional Generative Adversarial Network for Rapid Prediction of Microstructure and Field Evolution

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Authors

WYWenshu YangZWZhuo WangXWXiao Wang

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Overview

Randomized trial demonstrates CPGAN predicts microstructure evolution in additive manufacturing, highlighting improved accuracy and adaptability.

Key Points

  • This research aims to enhance the prediction of microstructure and field evolution in computational materials science using a novel deep learning framework.
  • Developed the Conditional Generative Adversarial Network (CPGAN) to integrate initial fields and processing conditions.
  • Validated the model against physics-based benchmarks across three engineering applications.
  • Assessed performance in terms of predictive accuracy and extrapolation capabilities.
  • CPGAN achieved superior predictive accuracy with reduced artifacts compared to standard convolutional methods.
  • Demonstrated robust spatial and temporal extrapolation, enabling high-resolution predictions.
  • Successfully simulated porosity evolution, von Mises stress distributions, and grain growth under diverse conditions.

Cite This Study

Yang et al. (2026) studied this question.

synapsesocial.com/papers/6a3f6871aea7db3c1953f792https://doi.org/10.3390/met16070691
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