Quantification of model error for inverse problems in the Weak Neural Variational Inference framework

• URL: http://arxiv.org/abs/2502.07415v1
• Date: Tue, 11 Feb 2025 09:52:06 GMT
• Title: Quantification of model error for inverse problems in the Weak Neural Variational Inference framework
• Authors: Vincent C. Scholz, P. S. Koutsourelakis,
• Abstract summary: We present a novel extension to the Weak Neural Variational Inference (WNVI) framework for probabilistic property estimation. Our framework explicitly quantifies model errors in PDE-based inverse problems. Our findings suggest that the proposed framework enhances the accuracy and reliability of material property estimation.
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• Abstract: We present a novel extension of the Weak Neural Variational Inference (WNVI) framework for probabilistic material property estimation that explicitly quantifies model errors in PDE-based inverse problems. Traditional approaches assume the correctness of all governing equations, including potentially unreliable constitutive laws, which can lead to biased estimates and misinterpretations. Our proposed framework addresses this limitation by distinguishing between reliable governing equations, such as conservation laws, and uncertain constitutive relationships. By treating all state variables as latent random variables, we enforce these equations through separate sets of residuals, leveraging a virtual likelihood approach with weighted residuals. This formulation not only identifies regions where constitutive laws break down but also improves robustness against model uncertainties without relying on a fully trustworthy forward model. We demonstrate the effectiveness of our approach in the context of elastography, showing that it provides a structured, interpretable, and computationally efficient alternative to traditional model error correction techniques. Our findings suggest that the proposed framework enhances the accuracy and reliability of material property estimation by offering a principled way to incorporate uncertainty in constitutive modeling.

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