Two machine learning stages, a space-time diffusion generator and a physics-informed super-resolution operator, produce and refine dynamic stress fields for two-phase random materials, with relative errors around 1 to 2 percent for the σxx component on synthetic data.
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Global Stress Generation and Spatiotemporal Super-Resolution Physics-Informed Operator under Dynamic Loading for Two-Phase Random Materials
Two machine learning stages, a space-time diffusion generator and a physics-informed super-resolution operator, produce and refine dynamic stress fields for two-phase random materials, with relative errors around 1 to 2 percent for the σxx component on synthetic data.