ArticleScience advances2022
Analyses of internal structures and defects in materials using physics-informed neural networks.
Article in Science advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Hyperelastic Cardiovascular NN-FE: A Framework for Integrating Nonlinear Finite Element Solvers with Neural Networks for the Hyperelastic Modeling of Cardiac Valve Tissue.Research square · 2026Article
- Stress-Constrained Physics-Informed UNet for Voxel-Wise Multiparameter Hyperelastic Inversion in Volumetric Medical Imaging.Annals of biomedical engineering · 2026Article
- Application of Local Approaches to Crack Development in Structural Materials.Materials (Basel, Switzerland) · 2026Review
- Clarifying the landscape of mechanical sensors from stress to strain.Nature sensors · 2026Article
- Deep learning-driven intelligent mesoscopic model (DeepMeso): a case study on ferroelectrics.National science review · 2026Article
- Deciphering optical coupled resonant systems with physics-data co-driven deep neural networks.Light, science & applications · 2026Article
- Physics-Informed Neural Network-Enabled Forward Prediction and Inverse Design of Ring Origami.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- High-precision stress prediction using deep energy network enhanced by stress equilibrium with Delaunay integration on refined grid.Cell reports methods · 2026Article
- In situ mechanical characterization of functional and architected materials.Nature materials · 2026Review
- PPFS-YOLO: Physics-Prior Frequency-Spatial Fusion for Robust Container Surface Damage Detection.Sensors (Basel, Switzerland) · 2026Article
- From PINNs to PIKANs: recent advances in physics-informed machine learning.Machine learning for computational science and engineering · 2025Article
- Effects of Surrogate Hybridization and Adaptive Sampling for Simulation-Based Optimization.Industrial & engineering chemistry research · 2025Article
- OrientationNN: a physics-informed lightweight neural network for real-time joint kinematics estimation from IMU data.Frontiers in bioengineering and biotechnology · 2025Article
- Identifying Heterogeneous Micromechanical Properties of Biological Tissues via Physics-Informed Neural Networks.Small methods · 2025Article
- Balance equations for physics-informed machine learning.Heliyon · 2024Article
- Coagulo-Net: Enhancing the mathematical modeling of blood coagulation using physics-informed neural networks.Neural networks : the official journal of the International Neural Network Society · 2024Article
- Discovering 3D hidden elasticity in isotropic and transversely isotropic materials with physics-informed UNets.Acta biomaterialia · 2024Article
- Article
- Spiking neural networks for nonlinear regression.Royal Society open science · 2024Article
- Myofascial trigger point (MTrP) size and elasticity properties can be used to differentiate characteristics of MTrPs in lower back skeletal muscle.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Characterizing internal structures and defects in materials is a challenging task, often requiring solutions to inverse problems with unknown topology, geometry, material properties, and nonlinear deformation. Here, we present a general framework based on physics-informed neural networks for identifying unknown geometric and material parameters. By using a mesh-free method, we parameterize the geometry of the material using a differentiable and trainable method that can identify multiple structural features. We validate this approach for materials with internal voids/inclusions using constitutive models that encompass the spectrum of linear elasticity, hyperelasticity, and plasticity. We predict the size, shape, and location of the internal void/inclusion as well as the elastic modulus of the inclusion. Our general framework can be applied to other inverse problems in different applications that involve unknown material properties and highly deformable geometries, targeting material characterization, quality assurance, and structural design.
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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.