The paper introduces PTNO, a neural operator that learns particle transport surrogates directly from noisy, low‑cost Monte Carlo (MC) labels, addressing high variance and high dynamic range challenges. By training on many noisy scenes, PTNO achieves comparable accuracy to converged MC while dramatically reducing computational cost, and it employs a softplus output and a pointwise relative L2 loss to handle HDR data. Experiments on neutron transport in fusion reactors and radiative transfer in participating media show speedups of up to 10⁵× and cost reductions of up to 10⁵× compared to traditional MC.
By Yubo Cao, Xi Deng, Mengqi Xia, Vignesh Gopakumar, Ander Gray, Anima Anandkumar
Neural Transport Nested Sampling (NTNS) is a new sampling algorithm that merges nested sampling with neural flow-based methods to sample from Boltzmann distributions of molecular systems. It employs a flow-matching velocity as the drift in a Metropolis–Hastings corrected Langevin kernel within a nested sampling loop, requiring only target energy evaluations and enabling scalable estimation of the full partition function for high-dimensional particle systems. Benchmarks on Lennard–Jones clusters up to 55 particles show NTNS reduces interatomic distance and energy Wasserstein errors by more than an order of magnitude compared to leading neural baselines, while also providing a calibrated, temperature-resolved partition function estimate that captures phase structure from a single run.
By David Yallup, Will Handley
Energy-Based Models (EBMs) provide an interpretable framework for generative modeling of scientific data, but poor Markov Chain Monte Carlo mixing often limits their reliability. We introduce a training algorithm based on Parallel Trajectory Tempering (PTT), which exploits the continuity of the optimization path to maintain equilibrium sampling throughout learning.
arXiv:2607. 27077v1 Announce Type: new Abstract: Energy-Based Models (EBMs) provide an interpretable framework for generative modeling of scientific data, but poor Markov Chain Monte Carlo mixing often limits their reliability.
By Nicolas B\'ereux, Aur\'elien Decelle, Cyril Furtlehner, Beatriz Seoane
arXiv:2601. 21026v2 Announce Type: replace-cross Abstract: Sampling configurations at thermodynamic equilibrium is a central challenge in statistical physics.
By Louis Grenioux, Maxence Noble
arXiv:2608. 07648v1 Announce Type: cross Abstract: Sampling high-dimensional probability distributions is a central task in scientific computing, with applications ranging from Bayesian inference to statistical physics and molecular simulation.
By Marylou Gabri\'e
arXiv:2512. 16607v2 Announce Type: replace-cross Abstract: Sampling configurations in thermodynamic equilibrium is a long-standing challenge in statistical physics.
By Louis Grenioux, Leonardo Galliano, Ludovic Berthier, Giulio Biroli, Marylou Gabri\'e
The paper introduces Contrastive Noise Alignment (CNA), a training-time method for generative flow models that dynamically aligns Gaussian noise with data samples using a cross-modal InfoNCE objective. By modeling noise as an interacting particle system and regularizing with angular entropy and radial norm penalties, CNA reduces arbitrary data-noise couplings and flow curvature. Empirical results show that CNA improves generation quality, lowering FID by over 50% for few-step pixel-space generation compared to standard rectified flow and outperforming optimal transport baselines by at least 24%.
By Lennart Wittke, Vinicius Azevedo
arXiv:2607. 15682v1 Announce Type: new Abstract: Sampling from an unnormalized Boltzmann density requires proposals that move probability mass globally while retaining enough path-probability information for statistical correction.
By Moxian Qian
arXiv:2608.23022v1 Announce Type: cross
Abstract: The dynamics of particles in the early universe are described by Boltzmann equations, which involve high-dimensional phase-space integrals. Classical...
By Jonas Spinner, Jack Shergold
The paper introduces a model calibration method using optimal transport to address discrepancies between simulation and experimental data in high-dimensional machine learning applications. Applied to jet tagging in particle physics, the technique calibrates a 128‑dimensional latent representation from a general‑purpose classifier, ensuring downstream derived quantities are properly calibrated. This enables more reliable use of foundation models for jet flavor analysis in LHC experiments and offers a general framework for correcting high‑dimensional simulations across scientific fields.
By Malte Algren, Tobias Golling, Francesco Armando Di Bello, Christopher Pollard
arXiv:2606. 04165v1 Announce Type: cross Abstract: High-precision calorimeter simulation at current and future colliders imposes rapidly growing computational demands, motivating the development of machine-learning surrogates for traditional Monte Carlo tools such as Geant4.
By Cheng Jiang, Sitian Qian, Kevin Pedro, Oz Amram, Huilin Qu, Maggie Voetberg