ArticleCommunications engineering2024
Multi-level physics informed deep learning for solving partial differential equations in computational structural mechanics.
Article in Communications engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Vision-Based Artificial Intelligence for Adaptive Peen Forming: Sensing Architectures, Learning Models, and Closed-Loop Smart Manufacturing.Sensors (Basel, Switzerland) · 2026Review
- Physics-constrained multimodal vision transformer for ultra-short-term solar radiation forecasting error correction.Scientific reports · 2026Article
- A Physics-Informed Neural Network forResearch square · 2026Article
- Single-shot matrix-matrix photonic processor based on spatial-spectral hypermultiplexed parallel diffraction.Nature communications · 2026Article
- Physics-Informed Neural Networks for Thermo-Responsive Hydrogel Swelling: Integrating Constitutive Models with Sparse Experimental Data.Materials (Basel, Switzerland) · 2025Article
- Information-distilled physics informed deep learning for high order differential inverse problems with extreme discontinuities.Communications engineering · 2025Article
- A physics-informed and data-driven framework for robotic welding in manufacturing.Nature communications · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Physics-informed neural network has emerged as a promising approach for solving partial differential equations. However, it is still a challenge for the computation of structural mechanics problems since it involves solving higher-order partial differential equations as the governing equations are fourth-order nonlinear equations. Here we develop a multi-level physics-informed neural network framework where an aggregation model is developed by combining multiple neural networks, with each one involving only first-order or second-order partial differential equations representing different physics information such as geometrical, constitutive, and equilibrium relations of the structure. The proposed framework demonstrates a remarkable advancement over the classical neural networks in terms of the accuracy and computation time. The proposed method holds the potential to become a promising paradigm for structural mechanics computation and facilitate the intelligent computation of digital twin systems.
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Registered trials
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