A universal material model subroutine for soft matter systems
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Abstract (arXiv)
Soft materials play an integral part in many aspects of modern life including autonomy, sustainability, and human health, and their accurate modeling is critical to understand their unique properties and functions. Today's finite element analysis packages come with a set of pre-programmed material models, which may exhibit restricted validity in capturing the intricate mechanical behavior of these materials. Regrettably, incorporating a modified or novel material model in a finite element analysis package requires non-trivial in-depth knowledge of tensor algebra, continuum mechanics, and computer programming, making it a complex task that is prone to human error. Here we design a universal material subroutine, which automates the integration of novel constitutive models of varying complexity in non-linear finite element packages, with no additional analytical derivations and algorithmic implementations. We demonstrate the versatility of our approach to seamlessly integrate innovative constituent models from the material point to the structural level through a variety of soft matter case studies: a frontal impact to the brain; reconstructive surgery of the scalp; diastolic loading of arteries and the human heart; and the dynamic closing of the tricuspid valve. Our universal material subroutine empowers all users, not solely experts, to conduct reliable engineering analysis of soft matter systems. We envision that this framework will become an indispensable instrument for continued innovation and discovery within the soft matter community at large.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Apr 2024.
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Citing papers per year: 2025: 3; 2026: 1.
4 papers.
- 2025COMMET: orders-of-magnitude speed-up in finite element method via batch-vectorized neural constitutive updatescited by 2
- 2025Unsupervised full-field Bayesian inference of orthotropic hyperelasticity from a single biaxial test: a myocardial case studycited by 1
- 2025Atrial constitutive neural networkscited by 0
- 2026CANN-EUCLID: unsupervised constitutive artificial neural network model discovery from full-field datacited by 0
- 2026CANN-EUCLID: unsupervised constitutive artificial neural network model discovery from full-field data12 Jun 2026
- 2025Unsupervised full-field Bayesian inference of orthotropic hyperelasticity from a single biaxial test: a myocardial case study10 Oct 2025
- 2025COMMET: orders-of-magnitude speed-up in finite element method via batch-vectorized neural constitutive updates1 Oct 2025
- 2025Atrial constitutive neural networks3 Apr 2025