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Extreme mass-ratio inspirals (EMRIs), with their long-lived and highly relativistic orbital evolution, can probe strong-field spacetime geometry and provide an important means to test general relativity. In this work, we investigate EMRI…

General Relativity and Quantum Cosmology · Physics 2026-05-11 Sheng Long , Zhong-wu Xia , Huajie Gong , Zhoujian Cao , Qiyuan Pan , Jiliang Jing

The quality of electron beams produced from plasma-based accelerators, i.e., normalized brightness and energy spread, has made transformative progress in the past several decades in both simulation and experiment. Recently, full-scale…

Accelerator Physics · Physics 2023-11-22 Xinlu Xu , Thamine N. Dalichaouch , Jiaxin Liu , Qianyi Ma , Jacob Pierce , Kyle Miller , Xueqing Yan , Warren B. Mori

This paper introduces a distillation framework for an ensemble of entropy-optimal Sparse Probabilistic Approximation (eSPA) models, trained exclusively on satellite-era observational and reanalysis data to predict ENSO phase up to 24 months…

Computational Physics · Physics 2026-02-20 Michael Groom , Davide Bassetti , Illia Horenko , Terence J. O'Kane

A question of global concern regarding the sustainable future of humankind stems from the effect due to aerosols on the global climate. The quantification of atmospheric aerosols and their relationship to climatic impacts are key to…

Atmospheric and Oceanic Physics · Physics 2020-04-10 David R. Vivas , Estiven Sánchez , John H. Reina

Multimodel ensembling has been widely used to improve climate model predictions, and the improvement strongly depends on the ensembling scheme. In this work, we propose a Bayesian neural network (BNN) ensembling method, which combines…

Atmospheric and Oceanic Physics · Physics 2022-08-10 Ming Fan , Dan Lu , Deeksha Rastogi , Eric M. Pierce

Accurate short-term electricity load forecasting is a cornerstone of U.S. grid reliability; however, prevailing deep learning models remain opaque, limiting operator trust during extreme weather. A unified, interpretable, physics-informed…

Machine Learning · Computer Science 2026-04-28 Md Abubakkar , Sajib Debnath , Md. Uzzal Mia

Groundwater in the Densu Basin is increasingly threatened by heavy metal contamination, but conventional methods fail to capture the statistical complexity and spatial heterogeneity of pollution indicators. A key challenge is modelling the…

Machine Learning · Computer Science 2026-05-04 T. Ansah-Narh , G. Y. Afrifa , J. B. Tandoh , K. Asare , M. Addi , K. E. Yorke , D. M. A. Akpoley , K. Aidoo , S. K. Fosuhene

We have carried out a statistical study on the mid- and far-infrared (IR) properties of Galactic IR bubbles observed by Spitzer. Using the Spitzer 8 ${\mu}{\rm m}$ images, we estimated the radii and covering fractions of their shells, and…

Accurate parameter estimation(PE) of gravitational waves(GW) is essential for GW data analysis. In extreme mass-ratio inspiral binary(EMRI) systems, orbital eccentricity is a critical parameter for PE. However, current software for for PE…

General Relativity and Quantum Cosmology · Physics 2025-11-18 Gen-Liang Li , Shu-Jie Zhao , Huai-Ke Guo , Jing-Yu Su , Zhen-Heng Lin

Edge plasma turbulence is critical to the performance of magnetic confinement fusion devices. Towards better understanding edge turbulence in both theory and experiment, a custom-built physics-informed deep learning framework constrained by…

Plasma Physics · Physics 2022-05-17 Abhilash Mathews

Extreme-mass-ratio inspiral (EMRI) signals pose significant challenges in gravitational wave (GW) astronomy owing to their low-frequency nature and highly complex waveforms, which occupy a high-dimensional parameter space with numerous…

The challenges in applications of solar energy lies in its intermittency and dependency on meteorological parameters such as; solar radiation, ambient temperature, rainfall, wind-speed etc., and many other physical parameters like dust…

Machine Learning · Computer Science 2024-04-02 Debojyoti Chakraborty , Jayeeta Mondal , Hrishav Bakul Barua , Ankur Bhattacharjee

Extended human presence beyond low-Earth orbit (BLEO) during missions to the Moon and Mars will pose significant challenges in the near future. A primary health risk associated with these missions is radiation exposure, primarily from…

The increasing volume of space objects in Earth's orbit presents a significant challenge for Space Situational Awareness (SSA). And in particular, accurate orbit prediction is crucial to anticipate the position and velocity of space…

Machine Learning · Computer Science 2024-09-24 Francisco Caldas , Cláudia Soares

We describe a new tool developed for solar flare forecasting on the base of some sunspot group properties. Assuming that the flare frequency follows the Poisson statistics, this tool uses a database containing the morphological…

Solar and Stellar Astrophysics · Physics 2019-05-16 M. Falco , P. Costa , P. Romano

This paper extends previous work (Groom et al., \emph{Artif. Intell. Earth Syst.}, 2024) in applying the entropy-optimal Sparse Probabilistic Approximation (eSPA) algorithm to predict ENSO phase, defined by thresholding the Ni\~no3.4 index.…

Computational Physics · Physics 2025-04-02 Michael Groom , Davide Bassetti , Illia Horenko , Terence J. O'Kane

Autonomous Experimentation Platforms (AEPs) are advanced manufacturing platforms that, under intelligent control, can sequentially search the material design space (MDS) and identify parameters with the desired properties. At the heart of…

Machine Learning · Computer Science 2023-02-28 Ahmed Shoyeb Raihan , Imtiaz Ahmed

The observational and theoretical state of Galactic and extragalactic bubbles are reviewed. Observations of superbubbles are discussed, with some emphasis on nearby bubbles such as the Local Bubble (LB) and the Loop I superbubble (LI).…

Astrophysics · Physics 2007-05-23 D. Breitschwerdt , M. A. de Avillez , M. J. Freyberg

A novel concept called Air-Breathing Electric Propulsion proposes to fly satellites at altitudes in the range 180-250 km, since this would have some advantages for the performance of radio communication and Earth observation equipment. The…

Plasma Physics · Physics 2025-04-18 Pietro Parodi , Giovanni Lapenta , Thierry Magin

Reliable wall-to-wall biomass density estimation from NASA's GEDI mission requires interpolating sparse LIDAR observations across heterogeneous landscapes. While machine learning approaches like Random Forest and XGBoost are widely used,…

Machine Learning · Computer Science 2026-02-05 Robin Young , Srinivasan Keshav