Physics-Informed Deep Neural Operator Networks Physics-Informed Deep Learning

Transfer learning-enhanced physics-informed neural network (TLE-PINN): A breakthrough in melt pool prediction for laser melting

The TLE-PINN method integrates EPINN and deep learning models through a transfer learning framework, combining strong physical constraints and efficient computational capabilities to accurately ...

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Frontier AI in computational civil engineering: a review of graph, sequence, physics-informed deep learning, and beyond (2020–2025)

Researchers present a comprehensive review of frontier AI applications in computational structural analysis from 2020 to 2025 ...

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Using geometry and physics to explain feature learning in deep neural networks

Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...

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Transfer learning-enhanced physics-informed neural network (TLE-PINN): A breakthrough in melt pool prediction for laser melting

Frontier AI in computational civil engineering: a review of graph, sequence, physics-informed deep learning, and beyond (2020–2025)

Using geometry and physics to explain feature learning in deep neural networks

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