Fermionic physics from ab initio path integral Monte Carlo simulations of fictitious identical particles. [PDF]
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
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Toward Intelligent Reconfigurable Wireless Physical Layer (PHY) [PDF]
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]
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
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Hyper-parameter tuning of physics-informed neural networks: Application to Helmholtz problems [PDF]
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
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
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Learning new physics efficiently with nonparametric methods [PDF]
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
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Full f and δf gyrokinetic particle simulations of Alfvén waves and energetic particle physics [PDF]
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]
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
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ARC 3.0: An expanded Python toolbox for atomic physics calculations [PDF]
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
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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
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