arXiv Machine Learning By Tobias G\"obel, Julian R. Ebelt, Zier Mensch, Mathis Gerdes, Miranda C. N. Cheng

Weight-Space Physics: Interpretable Hypernetworks for Lattice Quantum Field Theories

Read the original on arXiv Machine Learning →

arXiv:2607. 07127v1 Announce Type: cross Abstract: Lattice field theory is the workhorse of non-perturbative physics, used to simulate phenomena from the strong nuclear force to critical phenomena in materials.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Jul 27

Explainable quantum-compressed machine learning for complex fluid flows

arXiv:2607. 21688v1 Announce Type: cross Abstract: Machine-learning surrogates of physical systems face a paradox: explainable models facing the challenge of expressivity to capture complex nonlinear flows, whereas expressive deep surrogates match high-fidelity simulations only through massive parameterisations that turn the learned dynamics into a black box.

By Xiao Xue, Maida Wang, Mingyang Gao, Minh Chung, Peter V. Coveney
arXiv Machine Learning
Jun 30

Factorizable Normalizing Flows for parameter-dependent density morphing

arXiv:2606. 30489v1 Announce Type: cross Abstract: Normalizing Flows excel at modeling a single fixed density, yet many problems across the sciences, such as high energy physics, instead require modeling how that density deforms as a function of continuous parameters: the strength of a physical effect, a calibration constant, or a source of systematic uncertainty.

By Davide Valsecchi, Mauro Doneg\`a, Rainer Wallny
Hugging Face Trending Papers
Jun 10

Sparse probes and murky physics: a case study of interpretability challenges in a foundation model for continuum dynamics

Generative AI emulators are increasingly used in scientific domains where we already have strong theory, benchmarks, and physical intuition. This raises a central evaluation and interpretability question: when a foundation-style model can reproduce known continuum dynamics, what internal mechanism supports that behavior, is the internal behaviour consistent with known physics, and how does it relate to where the emulator succeeds or fails?