Data-Driven Discovery of Composition-Dependent Constitutive Models for Hyperelasticity and Viscoelasticity of Digital Materials
arXiv cs.AIen
arXiv:2609.04541v1 Announce Type: new Abstract: Digital materials fabricated by multi-material 3D printing are designed as controlled mixtures of stiff and compliant constituents, yielding effective responses that span more than an order of magnitude in apparent stiffness and exhibit strongly nonlinear, composition-dependent, and rate-dependent dissipative behavior. Classical finite-strain viscoelastic models represent such behavior with closed-form strain energy functions for equilibrium and non-equilibrium stresses as well as evolution of internal variables, which may limit flexibility when a single constitutive model is expected to generalize across materials and loading rates. Here, we p
This is a short summary published by AI Global Wire. The full article is owned and hosted by arXiv cs.AI — open it there to read it in full.
Read the full story at arXiv cs.AI- Verktyg
- Forskning
Related AI news
- Iris: Climbing to the Search FrontierarXiv cs.AI · September 7, 2026
- ResLearn-XR: Residual Learning for Network Traffic and Quality-of-Experience-Aware Modeling in Extended RealityarXiv cs.AI · September 7, 2026
- What Does Multi-Harness RL Learn? Credit Assignment and Portability in Coding AgentsarXiv cs.AI · September 7, 2026
- Does the Selected Object Reach the Reader? Auditing Identity Handoffs in Grounded Language-Model PipelinesarXiv cs.AI · September 7, 2026
- Harbor Adapters and Harbor-Index: Infrastructure and a Curated Meta-Dataset for Large-Scale Agentic EvaluationarXiv cs.AI · September 7, 2026
- PerfReasoning: How Well Do LLMs Reason on Hardware Performance?arXiv cs.AI · September 7, 2026