ArticleScientific reports2022
Predicting stress, strain and deformation fields in materials and structures with graph neural networks.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Description of the Behavior of a Polymer Hybrid-A Mathematical and Numerical Review.Materials (Basel, Switzerland) · 2026Review
- Stress Field Prediction and DIC-Informed Spatial Regularization for AlSi10Mg Alloy Tensile Specimens Based on Graph Neural Networks.Materials (Basel, Switzerland) · 2026Article
- Progress in Strain Engineering of 2D-Integrated Heterostructures for Ultrasensitive Sensors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Review
- Graph Neural Networks for Polymer Characterization and Property Prediction: Opportunities and Challenges.Journal of chemical information and modeling · 2026Review
- Graph-theoretic active learning for the closed-loop discovery of stochastic heterogeneous composites.PloS one · 2026Article
- From Pixels to Predictions: Integrating Machine Learning and Digital Image Correlation for Damage Identification in Engineering Materials.Materials (Basel, Switzerland) · 2025Review
- Integration of finite element method and artificial intelligence for evaluating PEEK composites in rib cage reconstruction process under impact conditions.Journal of materials science. Materials in medicine · 2025Article
- Conditional diffusion model for inverse prediction of process parameters and dendritic microstructures from mechanical properties.Scientific reports · 2025Article
- Global Research Trends in Biomimetic Lattice Structures for Energy Absorption and Deformation: A Bibliometric Analysis (2020-2025).Biomimetics (Basel, Switzerland) · 2025Article
- Prediction of stress-strain behavior of rock materials under biaxial compression using a deep learning approach.PloS one · 2025Article
- A thermodynamic based constitutive model considering the mutual influence of multiple physical fields.Scientific reports · 2024Article
- Deep reinforcement learning for microstructural optimisation of silica aerogels.Scientific reports · 2024Article
- Unifying the design space and optimizing linear and nonlinear truss metamaterials by generative modeling.Nature communications · 2023Article
Corrections and comments
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Developing accurate yet fast computational tools to simulate complex physical phenomena is a long-standing problem. Recent advances in machine learning have revolutionized the way simulations are approached, shifting from a purely physics- to AI-based paradigm. Although impressive achievements have been reached, efficiently predicting complex physical phenomena in materials and structures remains a challenge. Here, we present an AI-based general framework, implemented through graph neural networks, able to learn complex mechanical behavior of materials from a few hundreds data. Harnessing the natural mesh-to-graph mapping, our deep learning model predicts deformation, stress, and strain fields in various material systems, like fiber and stratified composites, and lattice metamaterials. The model can capture complex nonlinear phenomena, from plasticity to buckling instability, seemingly learning physical relationships between the predicted physical fields. Owing to its flexibility, this graph-based framework aims at connecting materials' microstructure, base materials' properties, and boundary conditions to a physical response, opening new avenues towards graph-AI-based surrogate modeling.
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