cond-mat.mtrl-sci · 2026-09-28 · No. 127

Materials Science, 2026-09-28.

1 new papers in cond-mat.mtrl-sci. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

1 entries
  1. 01

    Retrainable physics-integrated neural differentiable modeling of sintering across material systems

    Zeping Chen, Ani Aprahamian, Khachatur V. Manukyan, Tengfei Luo

    cond-mat.mtrl-sci · cs.LG

    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 predicting density and grain-size evolution. Two neural networks learn densification and grain-growth coefficients within coupled rate equations, while...

    arxiv.org/abs/2609.31518 · PDF

This edition is part of The Daily Abstract — cond-mat.mtrl-sci archive. Subscribe to receive these in your inbox each morning, automatically translated to Spanish, with reply-to-PDF: arxivdaily.ignorelist.com.

Colophon Set in Georgia, with system sans for interface chrome and a monospaced stack for code and paper identifiers. Sole accent: amber #D99C5E. Built and served on an always-free VM. The masthead is set 14% letterspaced because newspapers do that and it works.