arXiv:2605. 29283v2 Announce Type: replace-cross Abstract: Recent physics foundation models claim general spatiotemporal forecasting ability, yet their evaluations often collapse performance into a single average score under a fixed training distribution.
By Mengdi Chu, Yang Liu, Ayan Biswas, Han-Wei Shen
arXiv:2606. 16602v1 Announce Type: new Abstract: Neural operator models trained on simulation data often lose accuracy when applied to experimental measurements due to the sim-to-real gap.
By Changjian Zhou, Junfeng Fang, Negin Yousefpour, Peng Wu, Bin Yan, Guillermo A Narsilio
The paper introduces a neural‑network method that learns a minimal‑deviation transformation of Monte Carlo simulated events to match one‑dimensional target distributions while preserving the multidimensional correlation structure of the original simulation. By operating under limited experimental information, the approach avoids the pitfalls of traditional one‑dimensional reweighting and the data‑hungry fully multidimensional corrections. Controlled pseudo‑data studies demonstrate improved agreement with target distributions and consistent multidimensional structure, making the method suitable for complex, high‑dimensional analyses where conventional techniques fall short.
By Matthias Schott, Lucie Flek
arXiv:2606. 25151v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) embed governing equations in their loss function, enabling mesh-free solutions to partial differential equations.
By David McShannon, Nicholas Dietrich
arXiv:2608. 00632v1 Announce Type: new Abstract: Modern machine learning pipelines increasingly rely on reusing pretrained and foundation models across downstream tasks.
By Yiming Dong, Jiwei Zhao, Yang Young Lu
arXiv:2511.11286v4 Announce Type: replace-cross
Abstract: Out-of-domain (OOD) robustness is challenging to achieve in real-world computer vision, especially in unsupervised domain adaptation scenario...
By Ruoqi Wang, Haitao Wang, Shaojie Guo, Qiong Luo