Microstructure-based Variational Neural Networks for Robust Uncertainty Quantification in Materials Digital Twins

• URL: http://arxiv.org/abs/2512.18104v1
• Date: Fri, 19 Dec 2025 22:20:37 GMT
• Title: Microstructure-based Variational Neural Networks for Robust Uncertainty Quantification in Materials Digital Twins
• Abstract summary: Variational Deep Material Network (VDMN) is a physics-informed surrogate model that enables efficient and probabilistic forward and inverse predictions of material behavior.<n>VDMN captures microstructure-induced variability by embedding variational distributions within its hierarchical, mechanistic architecture.<n>We demonstrate its capabilities in two digital-twin-driven applications: (1) as an uncertainty-aware materials digital twin, it predicts and experimentally validates the nonlinear mechanical variability in additively manufactured polymer composites; and (2) as an inverse calibration engine, it disentangles and quantitatively identifies overlapping sources of uncertainty in constituent properties.
• Score: 0.19544534628180865
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Aleatoric uncertainties - irremovable variability in microstructure morphology, constituent behavior, and processing conditions - pose a major challenge to developing uncertainty-robust digital twins. We introduce the Variational Deep Material Network (VDMN), a physics-informed surrogate model that enables efficient and probabilistic forward and inverse predictions of material behavior. The VDMN captures microstructure-induced variability by embedding variational distributions within its hierarchical, mechanistic architecture. Using an analytic propagation scheme based on Taylor-series expansion and automatic differentiation, the VDMN efficiently propagates uncertainty through the network during training and prediction. We demonstrate its capabilities in two digital-twin-driven applications: (1) as an uncertainty-aware materials digital twin, it predicts and experimentally validates the nonlinear mechanical variability in additively manufactured polymer composites; and (2) as an inverse calibration engine, it disentangles and quantitatively identifies overlapping sources of uncertainty in constituent properties. Together, these results establish the VDMN as a foundation for uncertainty-robust materials digital twins.

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