arXiv (All)
open access
Retrainable physics-integrated neural differentiable modeling of sintering across material systems
Sintering is widely used to manufacture ceramics, but coupled densification and grain growth, material-dependent kinetics, and sparse measurements complicate predictive modeling and process design. We present Sinter-PiNDiff, a retrainable physics-integrated neural differentiable framework for predic
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- STVS-PINN: Segmented Training and Variable Scaling for Physics-Informed Neural Networks in Stiff PDEs#381306
- Inverse Design of Arbitrary Large-Scale Off-Axis Metalenses With a Physically-Tailored Deep Learning Model#387991
- CrystalX: High-accuracy Crystal Structure Analysis Using Deep Learning#163059
- Backward SDEs-based Diffusion for Physics-Constrained Generation#1109676
- Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $α+β$ Titanium Alloy#1048334
Record · ID 1106217
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive