SPLIT-PINN: Separable Probability Learning Technique via Physics-Informed Neural Networks for High-Dimensional Probabilistic Modeling
arXiv:2606.04000v1 Announce Type: cross Abstract: 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, formula
This paper leverages recent advancements in Physics-Informed Neural Networks (PINNs) to address complex materials science challenges, indicating the growing applicability of AI in scientific discovery.
It presents a method for improved probabilistic modeling of materials, which is crucial for advanced engineering and manufacturing, impacting sectors from aerospace to microelectronics where material failure is critical.
The ability to accurately model small-scale spatial heterogeneity in materials using AI could lead to more reliable material design and prediction of performance under various conditions, accelerating R&D cycles.
- · Materials science researchers
- · Advanced manufacturing industries
- · Aerospace and automotive R&D
- · AI/ML frameworks for scientific computing
- · Traditional empirical materials testing
- · Legacy materials simulation software
More robust and predictable material properties for next-generation engineering applications.
Faster innovation cycles in industries reliant on new materials, potentially reducing costs and time to market.
The democratization of advanced materials design, enabling a broader range of companies to develop high-performance products.
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Read at arXiv cs.LG