arXiv:2604. 27775v2 Announce Type: replace-cross Abstract: Shallow nanoindentation enables mechanical characterization of thin films, individual phases, and other volume-constrained materials, but the measured hardness is inflated by the indentation size effect (ISE).
By Radmir Karamov, Tagir Karamov
The paper evaluates machine learning (ML) models for predicting critical heat flux (CHF) in square rod bundles using the CTF subchannel code and the EPRI rod bundle CHF database. It compares pure and hybrid residual correction models in local and semilocal forms, finding that tube-trained ML models transfer well to rod bundle geometries and outperform traditional empirical correlations and lookup tables. The local hybrid LUT model shows the best overall performance, while the semilocal pure ML model remains highly competitive, indicating that significant improvements in rod bundle CHF prediction are achievable even with tube-only training data.
By Aidan Furlong, Vinicius de Melo Monteiro, Robert Salko, Juliana Pacheco Duarte, Xu Wu
arXiv:2606. 00938v1 Announce Type: cross Abstract: Data-driven surrogate models are an alternative to numerical homogenization of heterogeneous materials.
By Matthias Br\"andel, Oliver Rheinbach
Sinter-PiNDiff is a retrainable physics‑integrated neural differentiable framework that predicts density and grain‑size evolution during sintering. It uses two neural networks to learn densification and grain‑growth coefficients within coupled rate equations, and a smooth saturation factor to limit densification near theoretical density. When trained on published data for MgO, Al‑doped ZnO, and CaO‑doped ThO₂, the model achieved the lowest mean errors across twelve material‑metric comparisons compared to multilayer perceptron and residual network baselines, demonstrating the importance of density‑dependent kinetic feedback and providing uncertainty estimates via deep ensembles.
By Zeping Chen, Ani Aprahamian, Khachatur V. Manukyan, Tengfei Luo
arXiv:2608. 07589v1 Announce Type: cross Abstract: Predicting fatigue failure in steel components experimentally is costly because it requires testing across multiple compositions and processing conditions.
By Irene Boruah
arXiv:2604. 14562v2 Announce Type: replace Abstract: Accurate temperature field prediction in metal additive manufacturing (AM) is essential for understanding the process-structure-performance relationship.
By Hyeonsu Lee, Jihoon Jeong