arxiv
PublishedOctober 15, 2026 at 4:00 AM
Physics-Informed Graph Neural Networks to Reconstruct Local Fields Considering Finite Strain Hyperelasticity
Publisher summary· verbatim
arXiv:2507.05291v2 Announce Type: replace-cross Abstract: We propose a physics-informed machine learning framework called P-DivGNN to reconstruct local stress fields at the micro-scale, in the context of multi-scale simulation given a periodic micro-structure mesh and mean, macro-scale, stress value
Stay posted· Newsletter
Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.
Your email is used only to send The Bubble Brief. Privacy policy
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivBridging the Gap: Converting Read Text to Conversational Dialogue1harxivTruth for Believable AI: Expressed Doubt, Provenance, and Belief Revision as an Engineerable Stance1harxivRICE-Alpha: Reliability-Informed Correction with Event Graphs for LLM-Agent Stock Forecasting1harxivWitness Overlap: Directional Provenance Inside Open-Weight Model Families1hThe Bubble Brief
MON · WED · FRIRead AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.
Your email is used only to send The Bubble Brief. Privacy policy
Originally published on arxiv ↗