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

A Telescope System for Charge and Position Measurement of High Energy Nuclei

Dexing Miao, Zhiyu Xiang, Giovanni Ambrosi, Mattia Barbanera, Baasansuren Batsukh, Mengke Cai, Xudong Cai, Yuan-Hann Chang, Shanzhen Chen, Hsin-Yi Chou, Xingzhu Cui, Mingyi Dong, Matteo Duranti, Ke Gong, Mingjie Feng, Valerio Formato, Daojin Hong, Maria Ionica, Xiaojie Jiang, Yaozu Jiang, Liangchenglong Jin, Shengjie Jin, Vladimir Koutsenko, Tiange Li, Zuhao Li, Chih-Hsun Lin, Cong Liu, Pingcheng Liu, Xingjian Lv, Alberto Oliva, Ji Peng, Wenxi Peng, Rui Qiao, Shuqi Sheng, Gianluigi Silvestre, Congcong Wang, Feng Wang, Hongbo Wang, Zibing Wu, Suyu Xiao, Weiwei Xu, Sheng Yang, Xuhao Yuan, Xiyuan Zhang, Zijun Xu, Jianchun Wang ยท 2026

A high-granularity telescope system with a large sensitive area and low material budget has been developed for high-energy heavy ion beam tests. The telescope consists of nine layers of silicon microsโ€ฆ

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Concerted Electron-Ion Transport by Polyacrylonitrile Elucidated with Reactive Deep Learning Potentials

Rajni Chahal-Crockett, Michael D. Toomey, Logan T. Kearney, Yawei Gao, Joshua T. Damron, Amit K. Naskar, Santanu Roy ยท 2026

Charge transport in polymers, such as polyacrylonitrile (PAN), is crucial for electronics and energy storage. For instance, PAN can transport cations e.g., Li+, by facilitating dynamic cation-nitrile โ€ฆ

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Visualizing Millisecond Atomic Dynamics of Nanocrystals in Liquid

Sungsu Kang, Jinho Rhee, Joodeok Kim, Sam Oaks-Leaf, Minwoo Kim, Shengsong Yang, Chang Liu, Dongsu Kim, Sungin Kim, Binyu Wu, Won Bo Lee, David T. Limmer, A. Paul Alivisatos, Peter Ercius Jungwon Park ยท 2026

Atomic structures of nanomaterials are inherently dynamic, continuously reshaped through interactions with chemical species and external stimuli. Such dynamics are further amplified as the size and diโ€ฆ

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A versatile neural-network toolbox for testing Bell locality in networks

Antoine Girardin, Mohammad Massi Rashidi, Geraldine Haack, Nicolas Brunner, Alejandro Pozas-Kerstjens ยท 2026

Determining whether an observed distribution of events generated in a quantum network is Bell local, i.e., if it admits an alternative realization in terms of independent local variables, is extremelyโ€ฆ

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Spectral methods: crucial for machine learning, natural for quantum computers?

Vasilis Belis, Joseph Bowles, Rishabh Gupta, Evan Peters, Maria Schuld ยท 2026

This article presents an argument for why quantum computers could unlock new methods for machine learning. We argue that spectral methods, in particular those that learn, regularise, or otherwise maniโ€ฆ

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The physical meaning of the Belinfante-Rosenfeld ambiguity

Ioannis Matthaiakakis ยท 2026

Current literature lacks consensus on how to theoretically describe spin polarization and its transport in matter. The underlying reason is the presence of the Belinfante-Rosenfeld (BR) ambiguity in tโ€ฆ

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A Description of the Quantum Mpemba Effect using the Steepest-Entropy-Ascent Quantum Thermodynamics Framework

Luis Enrique Rocha-Soto, Cesar Eduardo Damian-Ascencio, Adriana Saldana-Robles, Sergio Cano-Andrade ยท 2026

The quantum Mpemba effect is a phenomenon characterized by an exponential relaxation from a non-equililbrium state to a steady state. This effect was predicted with an analysis of the Liouvillian supeโ€ฆ

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Fine-tuning universal machine learning potentials for transition state search in surface catalysis

Raffaele Cheula, Mie Andersen, John R. Kitchin ยท 2026

Determining transition states (TSs) of surface reactions is central to understanding and designing heterogeneous catalysts but remains computationally prohibitive with density functional theory (DFT).โ€ฆ

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Short-Term Turbulence Prediction for Seeing Using Machine Learning

Mary Joe Medlej, Rahul Srinivasan, Simon Prunet, Aziz Ziad, Christophe Giordano ยท 2026

Optical turbulence, driven by fluctuations of the atmospheric refractive index, poses a significant challenge to ground-based optical systems, as it distorts the propagation of light. This degradationโ€ฆ

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Machine Learning-Based Classification of Active Galaxies and Estimation of Supermassive Black Hole Masses

Farideh Mazoochi, Reihaneh Karimi, Mohammad Hossein Zhoolideh Haghighi, Fatemeh Tabatabaei ยท 2026

Distinguishing active galaxies from star-forming galaxies is essential for understanding galaxy evolution. Diagnostic methods like the BPT (Baldwin, Phillips, and Terlevich) diagram use optical emissiโ€ฆ

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Shape-Dependent, Deep-Learning-Assisted Metamaterial Solid Immersion Lens (mSIL) Super-Resolution Imaging

Baidong Wu, Fiza Khan, Lingya Yu, Zengbo Wang ยท 2026

We present the first systematic comparison of three TiO2 metamaterial solid immersion lens geometries - sub-hemispherical, super-hemispherical, and full-spherical - for label-free super-resolution imaโ€ฆ

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Aluminum solidification and nanopolycrystal deformation via a Graph Neural Network Potential and Million-Atom Simulations

Ian Stormer, Julija Zavadlav ยท 2026

Solidification governs the microstructure and, therefore, the mechanical response of metal components, yet the atomistic details of nucleation and defect formation are often difficult to determine expโ€ฆ

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Optimized control protocols for stable skyrmion creation using deep reinforcement learning

Ji Seok Song, Se Kwon Kim, Kyoung-Min Kim ยท 2026

Generating stable magnetic skyrmions is essential for the practical application of skyrmion-based spintronic devices in thermally agitating environments. Recent advancements have enabled the creation โ€ฆ

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A Longitudinal Analysis of the CEC Single-Objective Competitions (2010-2024) and Implications for Variational Quantum Optimization

Vojtech Novak, Tomas Bezdek, Ivan Zelinka, Swagatam Das, Martin Beseda ยท 2026

This paper provides a historical analysis of the IEEE CEC Single Objective Optimization competition results (2010-2024). We analyze how benchmark functions shaped winning algorithms, identifying the 2โ€ฆ

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Predicting Grain Growth Evolution Under Complex Thermal Profiles with Deep Learning through Thermal Descriptor Modulation

Pungponhavoan Tep, Marc Bernacki ยท 2026

Predicting microstructure evolution during thermomechanical treatment is essential for determining the final mechanical properties of a material, yet conventional simulations based on Partial Differenโ€ฆ

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Deep learning approaches to extract nuclear deformation parameters from initial-state information in heavy-ion collisions

Jun-Qi Tao, Yang Liu, Yu Sha, Xiang Fan, Yan-Sheng Tu, Kai Zhou, Hua Zheng, Ben-Wei Zhang ยท 2026

The deformation of heavy nuclei leaves characteristic imprints on the initial conditions of relativistic heavy-ion collisions. However, event-by-event fluctuations make the quantitative extraction of โ€ฆ

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Learning Quantum-Samplers for Stochastic Processes with Quantum Sequence Models

Ximing Wang, Chengran Yang, Chidambaram Aditya Somasundaram, Jayne Thompson, Mile Gu ยท 2026

Quantum circuits that generate coherent superpositions of stochastic processes are key to many downstream quantum-accelerated tasks, such as risk analysis, importance sampling, and DNA sequencing. Howโ€ฆ

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Early warning signals for primary and secondary bifurcation to oscillatory instabilities

Rohit Radhakrishnan, Prasana Kumar, Induja Pavithran, R. I. Sujith ยท 2026

In several natural and engineering systems, changes in control parameters can trigger bifurcations that lead to sustained or growing periodic oscillations, indicating the onset of oscillatory instabilโ€ฆ

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A Catalog of 1,408 Carbon-Enhanced Metal-Poor Stars from LAMOST DR11

Xianqi Liu, Xiangru Li, Ziyu Fang ยท 2026

Metal-poor (MP) stars are important targets for investigating the chemical evolution of the early universe. Among them, Carbon-Enhanced Metal-Poor (CEMP) stars have attracted extensive attention due tโ€ฆ

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Numerical field optimization for enhanced efficiency in time-reversible gradient computation of open-source GPU-accelerated FDTD simulations

Yannik Mahlau, Lukas Berg, Bodo Rosenhahn ยท 2026

Finite-difference time-domain (FDTD) simulations often involve physical quantities spanning multiple orders of magnitude, such as the speed of light or electromagnetic field amplitudes. The standard pโ€ฆ

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