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Ensemble weather predictions require statistical post-processing of systematic errors to obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished with distributional regression models in which the parameters…

Machine Learning · Statistics 2019-04-01 Stephan Rasp , Sebastian Lerch

A simple and well known model for thermal radiation spectra from a magnetized neutron star is further studied. The model assumes that the star is internally isothermal and possesses dipole magnetic field (B <= 1e14 G) in the outer…

High Energy Astrophysical Phenomena · Physics 2021-10-27 D. G. Yakovlev

We construct models for strongly-magnetized neutron star atmospheres composed of mid-Z elements (carbon, oxygen and neon) with magnetic fields B=10^{12}-10^{13} G and effective temperatures Teff=(1-5)*10^6 K; this is done by first…

Astrophysics · Physics 2008-11-26 Kaya Mori , Wynn C. G. Ho

Emulator embedded neural networks, which are a type of physics informed neural network, leverage multi-fidelity data sources for efficient design exploration of aerospace engineering systems. Multiple realizations of the neural network…

Machine Learning · Computer Science 2023-09-14 Atticus Beachy , Harok Bae , Jose Camberos , Ramana Grandhi

We present neural radiance fields (NeRF) with templates, dubbed Template-NeRF, for modeling appearance and geometry and generating dense shape correspondences simultaneously among objects of the same category from only multi-view posed…

Computer Vision and Pattern Recognition · Computer Science 2021-11-09 Jianfei Guo , Zhiyuan Yang , Xi Lin , Qingfu Zhang

Quantum materials research requires co-design of theory with experiments and involves demanding simulations and the analysis of vast quantities of data, usually including pattern recognition and clustering. Artificial intelligence is a…

Other Condensed Matter · Physics 2021-11-01 A. M. Samarakoon , D. Alan Tennant , Feng Ye , Qiang Zhang , S. A. Grigera

We develop a neuroevolution-potential (NEP) framework for generating neural network based machine-learning potentials. They are trained using an evolutionary strategy for performing large-scale molecular dynamics (MD) simulations. A…

Computational Physics · Physics 2022-01-25 Zheyong Fan , Zezhu Zeng , Cunzhi Zhang , Yanzhou Wang , Haikuan Dong , Yue Chen , Tapio Ala-Nissila

This study presents novel predictive models using Graph Neural Networks (GNNs) for simulating thermal dynamics in Laser Powder Bed Fusion (L-PBF) processes. By developing and validating Single-Laser GNN (SL-GNN) and Multi-Laser GNN (ML-GNN)…

Machine Learning · Computer Science 2024-07-22 Riddhiman Raut , Amit Kumar Ball , Amrita Basak

We investigate the thermal properties of the inner crust of a neutron star using the Hartree-Fock-Bogoliubov (HFB) formalism at finite temperature. We compare our results with the ones obtained solving the same equations, but within the BCS…

Nuclear Theory · Physics 2014-03-21 A. Pastore

Merging neutron stars are expected to produce hot, metastable remnants in rapid differential rotation, which subsequently cool and evolve into rigidly rotating neutron stars or collapse to black holes. Studying this metastable phase and its…

High Energy Astrophysical Phenomena · Physics 2021-03-19 Giovanni Camelio , Tim Dietrich , Stephan Rosswog , Brynmor Haskell

Background: The neutron skin of a heavy nucleus as well as many neutron-star properties are highly sensitive to the poorly constrained density dependence of the symmetry energy. Purpose: To provide for the first time meaningful theoretical…

Nuclear Theory · Physics 2012-07-12 F. J. Fattoyev , J. Piekarewicz

Numerical simulations of core-collapse supernovae, mergers of binary neutron stars and formation of stellar black holes, which employed standard Skyrme interactions, established clear correlations between the evolution of these processes,…

Nuclear Theory · Physics 2026-01-16 Adriana R. Raduta , Mikhail V. Beznogov

Atmospheric retrievals (AR) of exoplanets typically rely on a combination of a Bayesian inference technique and a forward simulator to estimate atmospheric properties from an observed spectrum. A key component in simulating spectra is the…

Instrumentation and Methods for Astrophysics · Physics 2023-12-20 Timothy D. Gebhard , Daniel Angerhausen , Björn S. Konrad , Eleonora Alei , Sascha P. Quanz , Bernhard Schölkopf

We study the thermal structure and evolution of magnetars as cooling neutron stars with a phenomenological heat source in an internal layer. We focus on the effect of magnetized (B > 10^{14} G) non-accreted and accreted outermost envelopes…

Solar and Stellar Astrophysics · Physics 2009-05-20 A. D. Kaminker , A. Y. Potekhin , D. G. Yakovlev , G. Chabrier

This doctoral thesis investigates the long-term evolution of the strong magnetic fields within isolated neutron stars (NSs), the most potent magnetic objects in the universe. Their magnetic influence extends beyond their surface to…

High Energy Astrophysical Phenomena · Physics 2024-05-02 Clara Dehman

We demonstrate a machine learning approach designed to extract hidden chemistry/physics to facilitate new materials discovery. In particular, we propose a novel method for learning latent knowledge from material structure data in which…

Materials Science · Physics 2021-08-03 Tien-Cuong Nguyen , Van-Quyen Nguyen , Van-Linh Ngo , Quang-Khoat Than , Tien-Lam Pham

Precision measurements of the number of effective relativistic neutrino species and the primordial element abundances require accurate theoretical predictions for early Universe observables in the Standard Model and beyond. Given the…

High Energy Physics - Phenomenology · Physics 2020-05-28 Miguel Escudero

Magnetar outbursts are among the most noteworthy manifestations of magnetism in neutron stars. They are episodes in which the X-ray luminosity of a strongly magnetised neutron star swiftly rises by several orders of magnitude to then decay…

High Energy Astrophysical Phenomena · Physics 2025-09-03 D. De Grandis , N. Rea , K. Kovlakas , F. Coti Zelati , D. Viganò , S. Ascenzi , J. A. Pons , R. Turolla , S. Zane

This study proposes a novel design methodology for neutron beam shutters that integrates Monte Carlo simulations (MCNP) with machine learning techniques to enhance shielding performance and accelerate the design process. The target facility…

Instrumentation and Detectors · Physics 2025-05-27 Li-Fang Chen

Future space telescopes now in the concept and design stage aim to observe reflected light spectra of extrasolar planets. Assessing whether given notional mission and instrument design parameters will provide data suitable for constraining…

Earth and Planetary Astrophysics · Physics 2020-04-29 Timothy K Johnsen , Mark S Marley
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