Non-linear mechanical field reconstruction coupling recurrent neural networks with physics-informed graph neural networks
Researchers have developed a novel framework combining Long Short-Term Memory (LSTM) networks with physics-informed Graph Neural Networks (GNNs) to reconstruct complex mechanical stress fields. This approach effectively captures path-dependent constitutive responses and spatially resolves stress fields, overcoming computational bottlenecks in multi-scale simulations. The model achieves a significant speedup of three orders of magnitude compared to traditional finite element methods and demonstrates generalization capabilities to longer loading sequences. AI
IMPACT This framework offers a significant speedup for complex simulations, potentially accelerating materials science and engineering research.