arXiv:2608. 19331v1 Announce Type: cross Abstract: We modify the NN/QFT duality [1] to incorporate the layerwise permutation symmetry of the network, resulting in a $(0\!
By Ro Jefferson, Shradha Ramakrishnan
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:2605.11199v2 Announce Type: replace-cross
Abstract: Neural generative samplers for lattice field theory can be costly to train and evaluate. When they miss modes or assign them incorrect relati...
By Moxian Qian
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. 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
arXiv:2607. 27320v1 Announce Type: cross Abstract: Field-level inference of cosmological initial conditions from galaxy surveys requires a forward model that is simultaneously accurate in the non-linear regime, computationally efficient, and fully differentiable.
By Cooper Jacobus, Beatriz Tucci, Oliver Philcox