New physics-based neural network framework models thermomechanics

Researchers have developed a novel physics-based neural network framework for modeling thermomechanics, focusing on internal energy and dissipation potentials rather than traditional Helmholtz energy. This approach simplifies the incorporation of thermodynamic principles and ensures thermodynamic admissibility by construction. The framework utilizes input convex neural networks to represent internal energy and dissipation, embedding objectivity and material symmetry directly into the architecture. The system has demonstrated accurate performance on synthetic and experimental datasets for various materials and thermomechanical responses. AI

IMPACT This framework could advance the accuracy and efficiency of simulating material behavior in engineering applications.

RANK_REASON The cluster contains a research paper detailing a new methodology in computational engineering. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• alphaXiv
• arXiv
• CatalyzeX Code Finder for Papers
• DagsHub
• Dissipation potentials from elastic collapse
• Gotit.pub
• Hagen Holthusen
• Helmholtz free energy
• Hugging Face
• Input-convex neural networks
• internal energy
• ScienceCast

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →