ArticleEngineering with computers2022
Data-driven Modeling of the Mechanical Behavior of Anisotropic Soft Biological Tissue.
Article in Engineering with computers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Monotone Peridynamic Neural Operator for Nonlinear Material Modeling with Conditionally Unique Solutions.Computer methods in applied mechanics and engineering · 2026Article
- Physics-Informed Machine Learning in Biomedical Science and Engineering.Annual review of biomedical engineering · 2026Review
- LLM-driven design of physics-constrained constitutive models: Two agents are better than one.Machine learning for computational science and engineering · 2026Article
- Article
- Investigation of the mechanical frequency response of ovine renal tissue at low frequencies.Scientific reports · 2025Article
- Exploring hyperelastic material model discovery for human brain cortex: Multivariate analysis vs. artificial neural network approaches.Journal of the mechanical behavior of biomedical materials · 2025Article
- Article
- HETEROGENEOUS PERIDYNAMIC NEURAL OPERATORS: DISCOVER BIOTISSUE CONSTITUTIVE LAW AND MICROSTRUCTURE FROM DIGITAL IMAGE CORRELATION MEASUREMENTS.Foundations of data science (Springfield, Mo.) · 2025Article
- Generative Hyperelasticity with Physics-Informed Probabilistic Diffusion Fields.Engineering with computers · 2025Article
- A universal material model subroutine for soft matter systems.Engineering with computers · 2025Article
- Democratizing biomedical simulation through automated model discovery and a universal material subroutine.Computational mechanics · 2025Article
- SELFNet: Denoising Shear Wave Elastography Using Spatial-temporal Fourier Feature Networks.Ultrasound in medicine & biology · 2024Article
- Tissue expansion mitigates radiation-induced skin fibrosis in a porcine model.Acta biomaterialia · 2024Article
- Automated data-driven discovery of material models based on symbolic regression: A case study on the human brain cortex.Acta biomaterialia · 2024Article
- Review of Machine Learning Techniques in Soft Tissue Biomechanics and Biomaterials.Cardiovascular engineering and technology · 2024Review
- Data-driven continuum damage mechanics with built-in physics.Extreme Mechanics Letters · 2024Article
- Benchmarking physics-informed frameworks for data-driven hyperelasticity.Computational mechanics · 2024Article
- 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
- Can machine learning accelerate soft material parameter identification from complex mechanical test data?Biomechanics and modeling in mechanobiology · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Closed-form constitutive models are the standard to describe soft tissue mechanical behavior. However, inherent pitfalls of an explicit functional form include poor fits to the data, non-uniqueness of fit, and sensitivity to parameters. Here we design deep neural networks (DNN) that satisfy desirable physics constraints in order to replace expert models of tissue mechanics. To guarantee stress-objectivity, the DNN takes strain (pseudo)-invariants as inputs, and outputs the strain energy and its derivatives. Polyconvexity of strain energy is enforced through the loss function. Direct prediction of both energy and derivative functions enables the computation of the elasticity tensor needed for a finite element implementation. We showcase the DNN ability to learn the anisotropic mechanical behavior of porcine and murine skin from biaxial test data. A multi-fidelity scheme that combines high fidelity experimental data with a low fidelity analytical approximation yields the best performance. Finite element simulations of tissue expansion with the DNN model illustrate the potential of this method to impact medical device design for skin therapeutics. We expect that the open data and software from this work will broaden the use of data-driven constitutive models of tissue mechanics.
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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.