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In numerical modeling of the Earth System, many processes remain unknown or ill represented (let us quote sub-grid processes, the dependence to unknown latent variables or the non-inclusion of complex dynamics in numerical models) but…

Data Analysis, Statistics and Probability · Physics 2019-03-19 Julien Brajard , Anastase Charantonis , Jérôme Sirven

Latent-variable energy-based models (LVEBMs) assign a single normalized energy to joint pairs of observed data and latent variables, offering expressive generative modeling while capturing hidden structure. We recast maximum-likelihood…

Machine Learning · Computer Science 2025-10-20 Shiqin Tang , Shuxin Zhuang , Rong Feng , Runsheng Yu , Hongzong Li , Youzhi Zhang

Diffusion models are a powerful tool for probabilistic forecasting, yet most applications in high-dimensional complex systems predict future states individually. This approach struggles to model complex temporal dependencies and fails to…

Machine Learning · Computer Science 2025-12-10 Salva Rühling Cachay , Miika Aittala , Karsten Kreis , Noah Brenowitz , Arash Vahdat , Morteza Mardani , Rose Yu

Type-I Edge Localised Modes (ELMs) have been mitigated in MAST through the application of n = 3, 4 and 6 resonant magnetic perturbations (RMPs). For each toroidal mode number of the non-axisymmetric applied fields, the frequency of the ELMs…

We introduce a shell-model theory that combines traditional spherical states, which yield a diagonal representation of the usual single-particle interaction, with collective configurations that track deformations, and test the validity of…

Nuclear Theory · Physics 2009-11-07 V. G. Gueorguiev , W. E. Ormand , C. W. Johnson , J. P. Draayer

Reduced quasilinear (QL) and nonlinear (gradient-driven) models with scale separations, commonly used to interpret experiments and to forecast turbulent transport levels in magnetised plasmas are tested against nonlinear models without…

Turbulent flows play an important role in many scientific and technological design problems. Both Sub-Grid Scale (SGS) models in Large Eddy Simulations (LES) and Reynolds Averaged Navier Stokes (RANS) based modeling will require turbulence…

Fluid Dynamics · Physics 2024-07-16 Minghan Chu

This article is devoted to Feller's diffusion equation which arises naturally in probabilities and physics (e.g. wave turbulence theory). If discretized naively, this equation may represent serious numerical difficulties since the diffusion…

Analysis of PDEs · Mathematics 2020-05-26 Denys Dutykh

Recent advances have increasingly applied large language models (LLMs) to electrocardiogram (ECG) interpretation, giving rise to Electrocardiogram-Language Models (ELMs). Conditioned on an ECG and a textual query, an ELM autoregressively…

Artificial Intelligence · Computer Science 2025-05-27 William Han , Chaojing Duan , Zhepeng Cen , Yihang Yao , Xiaoyu Song , Atharva Mhaskar , Dylan Leong , Michael A. Rosenberg , Emerson Liu , Ding Zhao

Engineering design and scientific analysis rely upon computer simulations of turbulent fluid flows using turbulence models. These turbulence models are empirical and approximate, leading to large uncertainties in their predictions that…

Fluid Dynamics · Physics 2024-05-28 Minghan Chu , Weicheng Qian

Three dimensional electromagnetic gyrofluid simulations of the ideal ballooning mode blowout scenario for tokamak edge localized modes (ELMs) are presented. Special emphasis is placed on energetic diagnosis, examining changes in the growth…

Plasma Physics · Physics 2012-10-19 A. Kendl , B. D. Scott , T. Ribeiro

Machine learning on electromyography (EMG) has recently achieved remarkable success on a variety of tasks, while such success relies heavily on the assumption that the training and future data must be of the same data distribution. However,…

Signal Processing · Electrical Eng. & Systems 2022-10-14 Di Wu , Jie Yang , Mohamad Sawan

The Ellsberg and Machina paradoxes reveal that expected utility theory is problematical when real subjects take decisions under uncertainty. Suitable generalizations of expected utility exist which attempt to solve the Ellsberg paradox, but…

Physics and Society · Physics 2013-01-08 Diederik Aerts , Sandro Sozzo , Jocelyn Tapia

Model uncertainty is a crucial issue in statistics, econometrics and machine learning, yet its definition remains ambiguous and is subject to various interpretations in the literature. So far, there has not been a universally accepted…

Methodology · Statistics 2025-08-12 Guangyuan Cui , Yuting Wei , Xinyu Zhang

Energy-Based Models (EBMs) present a flexible and appealing way to represent uncertainty. Despite recent advances, training EBMs on high-dimensional data remains a challenging problem as the state-of-the-art approaches are costly, unstable,…

Machine Learning · Computer Science 2021-06-08 Will Grathwohl , Jacob Kelly , Milad Hashemi , Mohammad Norouzi , Kevin Swersky , David Duvenaud

Electron holes (EH) are localized modes in plasma kinetic theory which appear as vortices in phase space. Earlier research on EH is based on the Schamel distribution function (df). A novel distribution function is proposed here,…

Plasma Physics · Physics 2021-10-12 S. M. Hosseini Jenab , I. Kourakis , G. Brodin , J. Juno

The problem of late time instability in time domain integral equations for electromagnetics is longstanding. While several techniques have been suggested for addressing this problem, they either require impractically high degrees of freedom…

Mathematical Physics · Physics 2012-12-13 N. V. Nair , A. J. Pray , B. Shanker

A simple model collisionless, dissipative, compressible MHD (Alfvenic) turbulence in a magnetized system is investigated. In contrast to more familiar paradigms of turbulence, dissipation arises from Landau damping, enters via nonlinearity,…

Plasma Physics · Physics 2009-10-30 M. V. Medvedev , P. H. Diamond

Despite the growing interest in diffusion models, gaining a deep understanding of the model class remains an elusive endeavour, particularly for the uninitiated in non-equilibrium statistical physics. Thanks to the rapid rate of progress in…

Machine Learning · Computer Science 2025-05-23 Fabio De Sousa Ribeiro , Ben Glocker

Ellerman bombs (EBs) seem to be a part of a whole spectrum of phenomena that might have the same underlying physical mechanism, but with observed characteristics which pose a considerable challenge to models. The aim of this study is to…

Solar and Stellar Astrophysics · Physics 2017-05-17 S. Danilovic