{"ID":2827303,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.18104","arxiv_id":"2512.18104","title":"Microstructure-based Variational Neural Networks for Robust Uncertainty Quantification in Materials Digital Twins","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.","short_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 an...","url_abs":"https://arxiv.org/abs/2512.18104","url_pdf":"https://arxiv.org/pdf/2512.18104v1","authors":"[\"Andreas E. Robertson\",\"Samuel B. Inman\",\"Ashley T. Lenau\",\"Ricardo A. Lebensohn\",\"Dongil Shin\",\"Brad L. Boyce\",\"Remi M. Dingreville\"]","published":"2025-12-19T22:20:37Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cond-mat.mtrl-sci\"]","methods":"[]","has_code":false}