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๐Ÿ” avoidance learning ๐Ÿ“‚ Physics
Showing 28154 results for "avoidance learning" in Physics
Physics Preprint PDF DOI

Machine-learning assistant DFT study of half-metallic full-Heusler alloy N2CaNa: structural, electronic, mechanical, and thermodynamics properties

E. B. Ettah, M.E. Ishaje, K. A. Minakova, V.A. Sirenko, I. S. Bondar ยท 2026

We studied the structural, electronic, mechanical, and thermodynamic properties of N2CaNa full Heusler alloys using density functional theory (DFT). Results for the structural analysis establishes strโ€ฆ

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Physics Preprint PDF DOI

Ground-State Structure Search of Defective High-Entropy Alloys Using Machine-Learning Potentials and Monte Carlo Sampling

Siya Zhu, Raymundo Arroyave ยท 2026

Resolving the atomic-scale structure of defective high-entropy alloys (HEAs) containing interstitial species remains a major computational challenge due to the vast configurational space and the limitโ€ฆ

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The ultrafine splitting of heavy quarkonium with next-to-next-to-next-to-next-to-leading-order accuracy

Jose M. Escario, Andreas Maier, Clara Peset, Antonio Pineda ยท 2026

We compute the hyperfine splitting of P-wave heavy quarkonium states with next-to-next-to-next-to-next-to-leading-order accuracy. The resummation of logarithms with next-to-next-to-next-to-next-to-leaโ€ฆ

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Physics Preprint PDF DOI

16 new quasars at the end of the reionization unveiled by self-supervised learning

L.N. Martinez-Ramirez, Julien Wolf, Silvia Belladitta, Eduardo Banados, F. E. Bauer, Raphael E. Hviding, Daniel Stern, Chiara Mazzucchelli, Romain A. Meyer, Ezequiel Treister, Federica Loiacono ยท 2026

Luminous quasars at $z > 6$ are key probes of early supermassive black hole (SMBH) growth, massive galaxy evolution, and intergalactic medium properties during cosmic reionization. However, their discโ€ฆ

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Tensor Train Representation of High-Dimensional Unsteady Flamelet Manifolds

Sinan Demir, Pierson Guthrey, Jason Burmark, Matthew Blomquist, Brian T. Bojkod, Ryan F. Johnson ยท 2026

This study, for the first time, investigates the use of tensor trains (TTs) to represent high-dimensional unsteady flamelet progress variable (UFPV) manifolds in chemically reacting computational fluiโ€ฆ

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Asymptotic Expansions for Neural Network Approximations of Quantum Channels

Romulo Damasclin Chaves dos Santos ยท 2026

This paper establishes the Quantum Voronovskaya--Damasclin (QVD) Theorem, providing a complete asymptotic characterization of Quantum Neural Network Operators in the approximation of arbitrary quantumโ€ฆ

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Physics Preprint PDF DOI

Explicit or Implicit? Encoding Physics at the Precision Frontier

Victor Breso-Pla, Kevin Greif, Vinicius Mikuni, Benjamin Nachman, Tilman Plehn, Tanvi Wamorkar, Daniel Whiteson ยท 2026

High-performance machine learning tools in particle physics rest on two complementary directions: encoding symmetries explicitly in the architecture, and implicitly learning the structure of the data โ€ฆ

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Efficient training of photonic quantum generative models

Felix Gottlieb, Rawad Mezher, Brian Ventura, Shane Mansfield, Alexia Salavrakos ยท 2026

The topic of generative learning has gained traction within the field of quantum machine learning, in particular with the advent of train-on-classical, deploy-on-quantum methods. This approach exploitโ€ฆ

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Analytic next-to-leading order electroweak corrections to Higgs boson pair production at high energies

Joshua Davies, Kay Schonwald, Matthias Steinhauser, Hantian Zhang ยท 2026

We compute the complete next-to-leading order electroweak corrections to the form factors entering gluon-induced Higgs boson pair production. We consider the top quark contribution in the limit where โ€ฆ

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Metriq: A Collaborative Platform for Benchmarking Quantum Computers

Alessandro Cosentino, Changhao Li, Vincent Russo, Bradley A. Chase, Tom Lubinski, Siyuan Niu, Neer Patel, Nathan Shammah, William J. Zeng ยท 2026

The fragmented landscape of quantum computer benchmarks, characterized by system-specific tools and inconsistent evaluation methodologies, hinders reliable cross-platform performance assessment. We inโ€ฆ

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Characterization and upgrade of a quantum graph neural network for charged particle tracking

Matteo Argenton, Laura Cappelli, Concezio Bozzi ยท 2026

In the forthcoming years the LHC experiments are going to be upgraded to benefit from the substantial increase of the LHC instantaneous luminosity, which will lead to larger, denser events, and, conseโ€ฆ

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A Deep Learning Framework for Amplitude Generation of Generic EMRIs

Yan-bo Zeng, Jian-dong Zhang, Yi-Ming Hu, Jianwei Mei ยท 2026

One of the main targets for space-borne gravitational wave detectors is the detection of Extreme Mass Ratio Inspirals (EMRIs). The data analysis of EMRIs requires waveform models that are both accuratโ€ฆ

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Glassy phase transition in immiscible steady-state two-phase flow in porous media

Santanu Sinha, Humberto Carmona, Jose S. Andrade Jr., Alex Hansen ยท 2026

Two-phase flow in porous media is a ubiquitous phenomenon that has been studied for well over a century. However, we still lack a successful theory that predicts flow on a macroscopic length scale (thโ€ฆ

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Heavy-Fermion Behavior and a Tunable Density Wave in a Novel Vanadium-based Mosaic Lattice

Yusen Xiao, Zhibin Qiu, Qingchen Duan, Zhaoyi Li, Hengxin Tan, Shu Guo, Ruidan Zhong ยท 2026

The pursuit of geometrically frustrated lattices beyond conventional paradigms remains a central challenge in the design of quantum materials. Herein, we report the discovery of Cs3V9Te13 (CVT), a novโ€ฆ

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Extrapolative Quantum Error Mitigation in Continuous-Variable Systems beyond the Training Horizon

Jingpeng Zhang, Shengyong Li, Jie Han, Qianchuan Zhao, Jing Zhang, Zeliang Xiang ยท 2026

Continuous-variable (CV) quantum systems provide a versatile platform for quantum information processing, in which quantum states can be represented in the quadrature phase space. In realistic implemeโ€ฆ

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ML in Astrophysical Turbulence I: Predicting Prestellar Cores in Magnetized Molecular Clouds using eXtreme Gradient Boosting

Nikhil Bisht, David C. Collins ยท 2026

Giant Molecular Clouds (GMCs) are dominated by supersonic turbulence, creating a complex network of shocks and filaments that regulate star formation. While the global inefficiency of star formation iโ€ฆ

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Enhanced Emittance Evaluation using 2D Transverse Phase Space Distributions, High Resolution Image Denoising, and Deep Learning

Francis Rene Osswald (IN2P3, UNISTRA), Mohammed Chahbaoui (UNISTRA), Xinyi Liang (SU) ยท 2026

Next-generation particle accelerators demand advanced beam-diagnostic capabilities to ensure high performance, operational reliability, and sustainable machine operation. Increasing beam intensities aโ€ฆ

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End-to-end optimisation of HEP triggers

Noah Clarke Hall, Ioannis Xiotidis, Nikos Konstantinidis, David W. Miller ยท 2026

High-energy physics experiments face extreme data rates, requiring real-time trigger systems to reduce event throughput while preserving sensitivity to rare processes. Trigger systems are typically coโ€ฆ

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Stochastic Loop Corrections to Belief Propagation for Tensor Network Contraction

Gi Beom Sim, Tae Hyeon Park, Kwang S. Kim, Yanmei Zang, Xiaorong Zou, Hye Jung Kim, D. ChangMo Yang, Soohaeng Yoo Willow, Chang Woo Myung ยท 2026

Tensor network contraction is a fundamental computational challenge underlying quantum many-body physics, statistical mechanics, and machine learning. Belief propagation (BP) provides an efficient appโ€ฆ

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Search for long-lived charginos and $\tau$-sleptons using final states with a disappearing track in $pp$ collisions at $\sqrt{s} = 13$ TeV with the ATLAS detector

ATLAS Collaboration ยท 2026

This paper reports a search for decays of long-lived charginos or $\tau$-sleptons to final states containing a short disappearing track, a single high-energy jet, and missing transverse momentum. The โ€ฆ

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