Results 11 to 20 of about 1,013,754 (226)

Fermionic physics from ab initio path integral Monte Carlo simulations of fictitious identical particles. [PDF]

open access: yesJournal of Chemical Physics, 2023
The ab initio path integral Monte Carlo (PIMC) method is one of the most successful methods in statistical physics, quantum chemistry and related fields, but its application to quantum degenerate Fermi systems is severely hampered by an exponential ...
T. Dornheim   +5 more
semanticscholar   +1 more source

Toward Intelligent Reconfigurable Wireless Physical Layer (PHY) [PDF]

open access: yesIEEE Open Journal of Circuits and Systems, 2021
Next-generation wireless networks are getting significant attention because they promise 10-factor enhancement in mobile broadband along with the potential to enable new heterogeneous services. Services include massive machine type communications desired for Industrial 4.0 along with ultra-reliable low latency services for remote healthcare and ...
Neelam Singh   +2 more
openaire   +4 more sources

A double-hybrid density functional based on good local physics with outstanding performance on the GMTKN55 database. [PDF]

open access: yesJournal of Chemical Physics, 2023
In two recent papers [A. D. Becke, J. Chem. Phys. 156, 214101 (2022) and A. D. Becke, J. Chem. Phys. 157, 234102 (2022)], we compared two Kohn-Sham density functionals based on physical modeling and theory with the best density-functional power-series ...
A. Becke, Golokesh Santra, Jan Martin
semanticscholar   +1 more source

Hyper-parameter tuning of physics-informed neural networks: Application to Helmholtz problems [PDF]

open access: yesNeurocomputing, 2022
We consider physics-informed neural networks (PINNs) [Raissi et al., J.~Comput. Phys. 278 (2019) 686-707] for forward physical problems. In order to find optimal PINNs configuration, we introduce a hyper-parameter optimization (HPO) procedure via ...
Paul Escapil-Inchauspé, Gonzalo A. Ruz
semanticscholar   +1 more source

Phy-Q as a measure for physical reasoning intelligence

open access: yesNature Machine Intelligence, 2023
Abstract Humans are well versed in reasoning about the behaviours of physical objects and choosing actions accordingly to accomplish tasks, while this remains a major challenge for artificial intelligence. To facilitate research addressing this problem, we propose a new testbed that requires an agent to reason about physical scenarios
Cheng Xue 0008   +5 more
openaire   +5 more sources

Learning new physics efficiently with nonparametric methods [PDF]

open access: yesThe European Physical Journal C, 2022
We present a machine learning approach for model-independent new physics searches. The corresponding algorithm is powered by recent large-scale implementations of kernel methods, nonparametric learning algorithms that can approximate any continuous ...
Marco Letizia   +7 more
semanticscholar   +1 more source

Full f and δf gyrokinetic particle simulations of Alfvén waves and energetic particle physics [PDF]

open access: yesPlasma Physics and Controlled Fusion, 2022
In this work, we focus on the development of the particle-in-cell scheme and its application to the studies of Alfvén waves and energetic particle (EP) physics in tokamak plasmas.
Zhi-Xin Lu   +4 more
semanticscholar   +1 more source

Precision Determination of Isotope Shifts in Ytterbium and Implications for New Physics. [PDF]

open access: yesPhysical Review Letters, 2021
We report measurements of isotope shifts for the five spinless Yb isotopes on the 6s^{2} ^{1}S_{0}→5d6s ^{1}D_{2} transition using Doppler-free two-photon spectroscopy. We combine these data with existing measurements on two transitions in Yb^{+} [Counts
N. L. Figueroa   +5 more
semanticscholar   +1 more source

ARC 3.0: An expanded Python toolbox for atomic physics calculations [PDF]

open access: yesComputer Physics Communications, 2020
ARC 3.0 is a modular, object-oriented Python library combining data and algorithms to enable the calculation of a range of properties of alkali and divalent atoms. Building on the initial version of the ARC library [N. Sibalic et al, Comput. Phys. Commun.
E. Robertson   +3 more
semanticscholar   +1 more source

Erratum: Covariant Spin Kinetic Theory I: Collisionless Limit, [Y.-C. Liu, K. Mameda, and X.-G. Huang, Chin. Phys. C 44, 094101 (2020)]

open access: yesChinese Physics C, High Energy Physics & Nuclear Physics, 2021
Erratum: Covariant Spin Kinetic Theory I: Collisionless Limit, [Y.-C. Liu, K. Mameda, and X.-G. Huang, Chin. Phys. C 44, 094101 (2020)] Yu-Chen Liu(刘玉琛) Kazuya Mameda Xu-Guang Huang(黄旭光) Physics Department and Center for Particle Physics and Field Theory,
Yu-Chen Liu   +2 more
semanticscholar   +1 more source

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