Learning Physics-Consistent Material Behavior Without Prior Knowledge

• URL: http://arxiv.org/abs/2407.20273v1
• Date: Thu, 25 Jul 2024 08:24:04 GMT
• Title: Learning Physics-Consistent Material Behavior Without Prior Knowledge
• Authors: Zhichao Han, Mohit Pundir, Olga Fink, David S. Kammer,
• Abstract summary: We introduce a machine learning approach called uLED, which overcomes the limitations by using the convex input neural network (ICNN) as a surrogate model. We demonstrate that it is robust to a significant level of noise and that it converges to the ground truth with increasing data resolution.
• Score: 6.691537914484337
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurately modeling the mechanical behavior of materials is crucial for numerous engineering applications. The quality of these models depends directly on the accuracy of the constitutive law that defines the stress-strain relation. Discovering these constitutive material laws remains a significant challenge, in particular when only material deformation data is available. To address this challenge, unsupervised machine learning methods have been proposed. However, existing approaches have several limitations: they either fail to ensure that the learned constitutive relations are consistent with physical principles, or they rely on a predefined library of constitutive relations or manually crafted input features. These dependencies require significant expertise and specialized domain knowledge. Here, we introduce a machine learning approach called uLED, which overcomes the limitations by using the input convex neural network (ICNN) as the surrogate constitutive model. We improve the optimization strategy for training ICNN, allowing it to be trained end-to-end using direct strain invariants as input across various materials. Furthermore, we utilize the nodal force equilibrium at the internal domain as the training objective, which enables us to learn the constitutive relation solely from temporal displacement recordings. We validate the effectiveness of the proposed method on a diverse range of material laws. We demonstrate that it is robust to a significant level of noise and that it converges to the ground truth with increasing data resolution. We also show that the model can be effectively trained using a displacement field from a subdomain of the test specimen and that the learned constitutive relation from one material sample is transferable to other samples with different geometries. The developed methodology provides an effective tool for discovering constitutive relations.

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