SPLIT-PINN: Separable Probability Learning Technique via Physics-Informed Neural Networks for High-Dimensional Probabilistic Modeling
We present a probabilistic modeling framework for incorporating small-scale spatial heterogeneity into macroscopic descriptions of material behavior for polycrystalline metallic materials. Spatially heterogeneous material state fields are represented using probability density functions (PDFs), providing a principled statistical description of microstructural variability and state evolution across different computational polycrystalline realizations. The framework is built on the inverse identification of a probabilistic transport model, formulated as a Liouville equation with an unknown drift term. To enable accurate, stable, and interpretable inference of this drift field in high-dimensi...
| Authors | Pouria Behnoudfar, Deekshith Naidu Ponnana, Noah J. Schmelzer et al. |
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| Primary category | cond-mat.mtrl-sci |
| Published | 23 May 2026 |
| Last updated at source | 23 May 2026 |
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