A physics-informed variational DeepONet for predicting the crack path in brittle materials
By Somdatta Goswami, Minglang Yin, Yue Yu, G. Em Karniadakis. Computer Methods in Applied Mechanics and Engineering, 2021
310 citations recorded by the source corpus.
Failure trajectories, identifying the probable failure zones, and damage statistics are some of the key quantities of relevance in brittle fracture applications. High-fidelity numerical solvers that reliably estimate these relevant quantities exist but they are computationally demanding requiring a high resolution of the crack. Moreover, independent intensive simulations need to be carried out even for a small change in domain parameters and/or material properties. Therefore, fast and generalizable surrogate models are needed to alleviate the computational burden but the discontinuous nature of fracture mechanics presents a major challenge to developing such models. We propose a physics-informed variational formulation of DeepONet (V-DeepONet) for brittle fracture analysis. V-DeepONet is trained to map the initial configuration of the defect to the relevant fields of interests (e.g., damage and displacement fields). Once the network is trained, the entire global solution can be rapidly obtained for any initial crack configuration and loading steps on that domain. While the original DeepONet is solely data-driven, we take a different path to train the V-DeepONet by imposing the governing equations in variational form and we also use some labelled data. We demonstrate the effectiveness of V-DeepOnet through two benchmarks of brittle fracture, and we verify its accuracy using results from high-fidelity solvers. Encoding the physical laws and also some data to train the network renders the surrogate model capable of accurately performing both interpolation and extrapolation tasks, considering that fracture modeling is very sensitive to fluctuations. The proposed hybrid training of V-DeepONet is superior to state-of-the-art methods and can be applied to a wide array of dynamical systems with complex responses.
Subfields: Civil and Structural Engineering, Mechanics of Materials, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/0a8e05eb0fe63d55f26c5557c1c0db45e0173b00
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Citations in the corpus
- MIONet: Learning multiple-input operators via tensor product (2022, methodology): “We can also embed physics into the loss function [42, 9] of MIONet to develop physics-informed MIONet.”