A variational physics-informed neural network solves higher-order anisotropic phase-field fracture models by minimizing total energy with B-spline enriched trial functions.
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
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SGNNs pretrain neural networks on synthetic corpora from multiple mechanistic models and noise levels to enable robust forecasting and back-to-simulation attribution across epidemiology, ecology, and other fields.
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Deep learning-based phase-field modelling of brittle fracture in anisotropic media
A variational physics-informed neural network solves higher-order anisotropic phase-field fracture models by minimizing total energy with B-spline enriched trial functions.
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Simulation as Supervision: Mechanistic Pretraining for Scientific Discovery
SGNNs pretrain neural networks on synthetic corpora from multiple mechanistic models and noise levels to enable robust forecasting and back-to-simulation attribution across epidemiology, ecology, and other fields.