ArticleScience advances2021
Deep learning model to predict complex stress and strain fields in hierarchical composites.
Article in Science advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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The trial behind it
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Who cites it
37 citing papers in PubMed.
- Machine learning-driven optimization of high-velocity impact resistance for three-dimensional biphasic composites.Nature communications · 2026Article
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- Failure Evaluation of Steel Plate Shear Walls in Multi-Storey Steel Buildings Under Seismic Excitation Using Convolutional Neural Networks.Materials (Basel, Switzerland) · 2026Article
- Deformation Prediction of 4D-Printed Active Composite Structures Based on Data Mining.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Giga-Voxel Multiscale Composite Architecture Mirrored Through a Data-to-Model Closed-Loop Digital Twin.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Wallpaper Group-Based Mechanical Metamaterials: Dataset Including Mechanical Responses.Scientific data · 2025Article
- Strong strain dependence of friction in graphene kirigami allows engineering a negative coefficient of friction.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Predicting compressive stress-strain behavior of elasto-plastic porous media via morphology-informed neural networks.Communications engineering · 2025Article
- Fast Reverse Design of 4D-Printed Voxelized Composite Structures Using Deep Learning and Evolutionary Algorithm.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Optimization design of interior space based on the two-stage deep learning network and Single sample-driven method.PloS one · 2025Article
- Advancements in Subchondral Bone Biomechanics: Insights from Computed Tomography and Micro-Computed Tomography Imaging in Equine Models.Current osteoporosis reports · 2024Review
- Automated discovery of reprogrammable nonlinear dynamic metamaterials.Nature materials · 2024Article
- Accurate prediction of discontinuous crack paths in random porous media via a generative deep learning model.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- ProtAgents: protein discoveryDigital discovery · 2024Article
- Mechanical Field Guiding Structure Design Strategy for Meta-Fiber Reinforced Hydrogel Composites by Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Multiscale Computational and Artificial Intelligence Models of Linear and Nonlinear Composites: A Review.Small science · 2024Article
- ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model.Science advances · 2024Article
- Physics-informed UNets for discovering hidden elasticity in heterogeneous materials.Journal of the mechanical behavior of biomedical materials · 2024Article
- Unbiased construction of constitutive relations for soft materials from experiments via rheology-informed neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Rapid and accurate predictions of perfect and defective material properties in atomistic simulation using the power of 3D CNN-based trained artificial neural networks.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Materials-by-design is a paradigm to develop previously unknown high-performance materials. However, finding materials with superior properties is often computationally or experimentally intractable because of the astronomical number of combinations in design space. Here we report an AI-based approach, implemented in a game theory-based conditional generative adversarial neural network (cGAN), to bridge the gap between a material's microstructure-the design space-and physical performance. Our end-to-end deep learning model predicts physical fields like stress or strain directly from the material microstructure geometry, and reaches an astonishing accuracy not only for predicted field data but also for derivative material property predictions. Furthermore, the proposed approach offers extensibility by predicting complex materials behavior regardless of component shapes, boundary conditions, and geometrical hierarchy, providing perspectives of performing physical modeling and simulations. The method vastly improves the efficiency of evaluating physical properties of hierarchical materials directly from the geometry of its structural makeup.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.