Summary of Automated Model Discovery For Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling, by Hagen Holthusen and Tim Brepols and Kevin Linka and Ellen Kuhl
Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling
by Hagen Holthusen, Tim Brepols, Kevin Linka, Ellen Kuhl
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Materials Science (cond-mat.mtrl-sci); Computational Engineering, Finance, and Science (cs.CE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research presents a novel framework to describe the behavior of soft biological tissues, which exhibit a tendency to maintain a preferred state of tensile stress, known as tensional homeostasis. The study introduces the concept of kinematic growth theory, decomposing deformation gradients into elastic and growth-related components. This framework is applied to define homeostatic surfaces and evolution equations for inelastic deformations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Soft biological tissues have a special ability to maintain their shape even after external forces are applied or removed. Scientists have found that this “memory” can be explained by a mathematical concept called kinematic growth theory. The research shows how this theory helps us understand the behavior of soft tissues and how they adapt to changing conditions. |
