Discovering uncertainty: Gaussian constitutive neural networks with correlated weights

• URL: http://arxiv.org/abs/2503.12679v1
• Date: Sun, 16 Mar 2025 22:34:16 GMT
• Title: Discovering uncertainty: Gaussian constitutive neural networks with correlated weights
• Authors: Jeremy A. McCulloch, Ellen Kuhl,
• Abstract summary: We introduce a more interpretable network with fewer parameters, simpler training, and the potential to discover correlated weights.<n> Importantly, the discovered distributions of material parameters across a set of samples can serve as priors to discover better models for new samples with limited data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When characterizing materials, it can be important to not only predict their mechanical properties, but also to estimate the probability distribution of these properties across a set of samples. Constitutive neural networks allow for the automated discovery of constitutive models that exactly satisfy physical laws given experimental testing data, but are only capable of predicting the mean stress response. Stochastic methods treat each weight as a random variable and are capable of learning their probability distributions. Bayesian constitutive neural networks combine both methods, but their weights lack physical interpretability and we must sample each weight from a probability distribution to train or evaluate the model. Here we introduce a more interpretable network with fewer parameters, simpler training, and the potential to discover correlated weights: Gaussian constitutive neural networks. We demonstrate the performance of our new Gaussian network on biaxial testing data, and discover a sparse and interpretable four-term model with correlated weights. Importantly, the discovered distributions of material parameters across a set of samples can serve as priors to discover better constitutive models for new samples with limited data. We anticipate that Gaussian constitutive neural networks are a natural first step towards generative constitutive models informed by physical laws and parameter uncertainty.

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