ArticleComputer methods in applied mechanics and engineering2020
A generic physics-informed neural network-based constitutive model for soft biological tissues.
Article in Computer methods in applied mechanics and engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed.
- Monotone Peridynamic Neural Operator for Nonlinear Material Modeling with Conditionally Unique Solutions.Computer methods in applied mechanics and engineering · 2026Article
- Human lungs fluid mechanics: an overview of current modelling techniques.The European physical journal. E, Soft matter · 2026Review
- Physics-informed data-driven discovery of constitutive models with application to strain-rate-sensitive soft materials.Computational mechanics · 2026Article
- Investigating the role of structural wall stress in aortic growth prognosis in acute uncomplicated type B aortic dissection.Biomechanics and modeling in mechanobiology · 2025Article
- Physics-Informed Neural Networks for Depth-Dependent Constitutive Relationships of Gradient Nanostructured 316L Stainless Steel.Materials (Basel, Switzerland) · 2025Article
- From PINNs to PIKANs: recent advances in physics-informed machine learning.Machine learning for computational science and engineering · 2025Article
- Learning soft tissue deformation from incremental simulations.Medical physics · 2025Article
- Generative Hyperelasticity with Physics-Informed Probabilistic Diffusion Fields.Engineering with computers · 2025Article
- SELFNet: Denoising Shear Wave Elastography Using Spatial-temporal Fourier Feature Networks.Ultrasound in medicine & biology · 2024Article
- Review of Machine Learning Techniques in Soft Tissue Biomechanics and Biomaterials.Cardiovascular engineering and technology · 2024Review
- Benchmarking physics-informed frameworks for data-driven hyperelasticity.Computational mechanics · 2024Article
- Synergistic Integration of Deep Neural Networks and Finite Element Method with Applications of Nonlinear Large Deformation Biomechanics.Computer methods in applied mechanics and engineering · 2023Article
- Bio-inspired microfluidics: A review.Biomicrofluidics · 2023Review
- PyTorch-FEA: Autograd-enabled finite element analysis methods with applications for biomechanical analysis of human aorta.Computer methods and programs in biomedicine · 2023Article
- Data-driven anisotropic finite viscoelasticity using neural ordinary differential equations.Computer methods in applied mechanics and engineering · 2023Article
- Machine learning and reduced order modelling for the simulation of braided stent deployment.Frontiers in physiology · 2023Article
- Data-driven Modeling of the Mechanical Behavior of Anisotropic Soft Biological Tissue.Engineering with computers · 2022Article
- G2Φnet: Relating genotype and biomechanical phenotype of tissues with deep learning.PLoS computational biology · 2022Article
- Data-driven Tissue Mechanics with Polyconvex Neural Ordinary Differential Equations.Computer methods in applied mechanics and engineering · 2022Article
- A data-driven approach to characterizing nonlinear elastic behavior of soft materials.Journal of the mechanical behavior of biomedical materials · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Constitutive modeling is a cornerstone for stress analysis of mechanical behaviors of biological soft tissues. Recently, it has been shown that machine learning (ML) techniques, trained by supervised learning, are powerful in building a direct linkage between input and output, which can be the strain and stress relation in constitutive modeling. In this study, we developed a novel generic physics-informed neural network material (NNMat) model which employs a hierarchical learning strategy by following the steps: (1) establishing constitutive laws to describe general characteristic behaviors of a class of materials; (2) determining constitutive parameters for an individual subject. A novel neural network structure was proposed which has two sets of parameters: (1) a class parameter set for characterizing the general elastic properties; and (2) a subject parameter set (three parameters) for describing individual material response. The trained NNMat model may be directly adopted for a different subject without re-training the class parameters, and only the subject parameters are considered as constitutive parameters. Skip connections are utilized in the neural network to facilitate hierarchical learning. A convexity constraint was imposed to the NNMat model to ensure that the constitutive model is physically relevant. The NNMat model was trained, cross-validated and tested using biaxial testing data of 63 ascending thoracic aortic aneurysm tissue samples, which was compared to expert-constructed models (Holzapfel-Gasser-Ogden, Gasser-Ogden-Holzapfel, and four-fiber families) using the same fitting and testing procedure. Our results demonstrated that the NNMat model has a significantly better performance in both fitting (R
Indexed as
Identifiers
What OpenQuestion holds
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.