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

A scalable advantage in multi-photon quantum machine learning

Yong Wang, Zhenghao Yin, Tobias Haug, Ciro Pentangelo, Simone Piacentini, Andrea Crespi, Francesco Ceccarelli, Roberto Osellame, Philip Walther ยท 2025

Photons are promising candidates for quantum information technology due to their high robustness and long coherence time at room temperature. Inspired by the prosperous development of photonic computiโ€ฆ

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Physics Preprint PDF DOI

FPGA-Accelerated Real-Time Beam Emission Spectroscopy Diagnostics at DIII-D Using the SLAC Neural Network Library for ML Inference

Abhilasha Dave, James Russell, Mudit Mishra, Larry Ruckman, Keith Erickson, SangKyeun Kim, Semin Joung, Jalal Butt, Ryan Herbst, Ryan Coffee, David Smith, Egemen Kolemen ยท 2025

Achieving reliable real-time control of tokamak plasmas is essential for sustaining high-performance operation in next-generation fusion reactors. A major challenge is the accurate and timely predictiโ€ฆ

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Physics Preprint PDF DOI

New Physics Searches at the LHC through Event-based Anomaly Detection and Development of ADFilter Web-tool

Wasikul Islam, Sergei Chekanov, Nicholas Luongo ยท 2025

This work presents advancements in model-agnostic searches for new physics at the Large Hadron Collider (LHC) through the application of event-based anomaly detection techniques utilizing unsupervisedโ€ฆ

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Physics Preprint PDF DOI

Predicting liquid properties and behavior via droplet pinch-off and machine learning

Jingtao Wang, Qiwei Chen, C Ricardo Constante-Amores, Denise Gorse, Alfonso Arturo Castrejon-Pita, and Jose Rafael, Castrejon-Pitaa ยท 2025

Here we demonstrate that the time-evolving interface observed during droplet formation, and consequently the resulting morphology nearing pinch-off, encode sufficient physical information for machine-โ€ฆ

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Physics Preprint PDF DOI

Revealing Fast Ionic Conduction in Solid Electrolytes through Machine Learning Accelerated Raman Calculations

Manuel Grumet, Takeru Miyagawa, Olivier Pittet, Paolo Pegolo, Karin S. Thalmann, Waldemar Kaiser, David A. Egger ยท 2025

Fast ionic conduction is a defining property of solid electrolytes for all-solid-state batteries. Previous studies have suggested that liquid-like cation motion associated with fast ionic transport caโ€ฆ

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Physics Preprint PDF DOI

Differentiable Physics-Neural Models enable Learning of Non-Markovian Closures for Accelerated Coarse-Grained Physics Simulations

Tingkai Xue, Chin Chun Ooi, Zhengwei Ge, Fong Yew Leong, Hongying Li, Chang Wei Kang ยท 2025

Numerical simulations provide key insights into many physical, real-world problems. However, while these simulations are solved on a full 3D domain, most analysis only require a reduced set of metricsโ€ฆ

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Physics Preprint PDF DOI

Active Learning Driven Materials Discovery for Low Thermal Conductivity Rare-Earth Pyrochlore for Thermal Barrier Coatings

Amiya Chowdhury, Acacio Rincon Romero, Grazziela Figueredo, Tanvir Hussain ยท 2025

High-Entropy/multicomponent rare-earth oxides (HECs and MCCs) show promise as alternative materials for thermal barrier coatings (TBC) with the ability to tailor properties based on the combination ofโ€ฆ

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Physics Preprint PDF DOI

Monitoring and Regulation of Micro-Displacement Deviation in Few-Mode Beam Alignment through Mode Decomposition

Lin Xu, Li Pei, Jianshuai Wang, Zhouyi Hu, Tigang Ning ยท 2025

Beam alignment enables efficient, stable transmission and control of optical energy and information, which critically depend on precise monitoring and regulation of the three-dimensional (3D) relativeโ€ฆ

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Physics Preprint PDF DOI

Lattice-to-Total Thermal Conductivity Ratio: A Phonon-Glass Electron-Crystal Descriptor for Data-Driven Thermoelectric Design

Yifan Sun, Zhi Li, Tetsuya Imamura, Yuji Ohishi, Chris Wolverton, Ken Kurosaki ยท 2025

Thermoelectrics (TEs) are promising candidates for energy harvesting with performance quantified by figure of merit, $ZT$. To accelerate the discovery of high-$ZT$ materials, efforts have focused on iโ€ฆ

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Physics Preprint PDF DOI

Enhancing Galaxy Classification with U-Net Variational Autoencoders. II. JWST High Redshift Galaxy Sample

Sergey Mirzoyan ยท 2025

Building on our previous work, we apply a U-Net Variational Autoencoder (VAE) framework to denoise galaxy images from the James Webb Space Telescope (JWST) and enhance morphological classification. Thโ€ฆ

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Accelerated Discovery of Crystalline Materials with Record Ultralow Lattice Thermal Conductivity via a Universal Descriptor

Xingchen Shen, Jiongzhi Zheng, Michael Marek Koza, Petr Levinsky, Jiri Hejtmanek, Philippe Boullay, Bernard Raveau, Jinghui Wang, Jun Li, Pierric Lemoine, Christophe Candolfi, Emmanuel Guilmeau ยท 2025

Ultralow glass-like lattice thermal conductivity in crystalline materials is crucial for enhancing energy conversion efficiency in thermoelectrics and thermal insulators. We introduce a universal descโ€ฆ

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Physics Preprint PDF DOI

Spectuner-D1: Spectral Line Fitting of Interstellar Molecules Using Deep Reinforcement Learning

Yisheng Qiu, Tianwei Zhang, Tie Liu, Fengyao Zhu, Dezhao Meng, Huaxi Chen, Thomas Moller, Peter Schilke, Donghui Quan ยท 2025

Spectral lines from interstellar molecules provide crucial insights into the physical and chemical conditions of the interstellar medium. Traditional spectral line analysis relies heavily on manual inโ€ฆ

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Physics Preprint PDF DOI

Refined classification of YSOs and AGB stars by IR magnitudes, colors, and time-domain analysis with machine learning

Hyunwook Jheonn, Jeong-Eun Lee, Jinho Lee, Seonjae Lee, Hyeyoon Lee, ShinGeon Kim, Carlos Contreras Pena, Mi-Ryang Kim ยท 2025

We introduce a binary classification model, {\it the Double Filter Model}, utilizing various machine learning and deep learning methods to classify Young Stellar Objects (YSOs) and Asymptotic Giant Brโ€ฆ

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Physics Preprint PDF DOI

Different Origins of Nucleated and Non-nucleated Dwarf Elliptical Galaxies: Identified by the Deep-learning

Sanjaya Paudel, Cristiano G. Sabiu, Suk-Jin Yoon, Daya Nidhi Chhatkuli, Woong-Bae G. Zee, Jaewon Yoo, Binod Adhikari ยท 2025

Dwarf elliptical galaxies (dEs) are the dominant population in galaxy clusters and serve as ideal probes for studying the environmental impact on galactic evolution. A substantial fraction of dEs are โ€ฆ

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Physics Preprint PDF DOI

Hierarchical high-throughput screening of alkaline-stable lithium-ion conductors combining machine learning and first-principles calculations

Zhuohan Li, KyuJung Jun, Bowen Deng, Gerbrand Ceder ยท 2025

Solid-state batteries require lithium-ion conductors that combine high ionic conductivity with stability under harsh electrochemical and chemical conditions. Here, we investigate the chemical factors โ€ฆ

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Physics Preprint PDF DOI

Fusion of classical and quantum kernels enables accurate and robust two-sample tests

Yu Terada, Yugo Ogio, Ken Arai, Hiroyuki Tezuka, Yu Tanaka ยท 2025

Two-sample tests have been extensively employed in various scientific fields and machine learning such as evaluation on the effectiveness of drugs and A/B testing on different marketing strategies to โ€ฆ

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Hardware Acceleration of Frustrated Lattice Systems using Convolutional Restricted Boltzmann Machine

Pratik Brahma, Junghoon Han, Tamzid Razzaque, Saavan Patel, Sayeef Salahuddin ยท 2025

Geometric frustration gives rise to emergent quantum phenomena and exotic phases of matter. While Monte Carlo methods are traditionally used to simulate such systems, their sampling efficiency is limiโ€ฆ

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Physics Preprint PDF DOI

Automated all-sky detection of {\gamma} Doradus / {\delta} Scuti hybrids in TESS data from positive unlabelled (PU) learning

Mykyta Kliapets, Pablo Huijse, Andrew Tkachenko, Alex Kemp, Dario J. Fritzewski, Daniel Hey, Conny Aerts ยท 2025

The Transiting Exoplanet Survey Satellite (TESS) mission has observed hundreds of millions of stars, substantially contributing to the available pool of high-precision photometric space data. Among thโ€ฆ

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Physics Preprint PDF DOI

Mapping the Galaxy Color-Star Formation Rate Relation with Manifold Learning and Infrared Image Stacking

Yu-Heng Lin, Daniel Masters, Andreas L. Faisst, Harry Teplitz, Olivier Ilbert, Matthieu Bethermin, Shoubaneh Hemmati, Vihang Mehta, Jason D. Rhodes, Gregory L. Walth ยท 2025

Modern surveys present us with billions of faint galaxies for which we only have broadband images in $\sim$6-8 optical-to-near-infrared (NIR) filters. Galaxy star formation rates (SFRs) are difficult โ€ฆ

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Physics Preprint PDF DOI

An empirical view of the extended atmosphere and inner envelope of the asymptotic giant branch star R Doradus. II. Constraining the dust properties with radiative transfer modelling

Thiebaut Schirmer, Theo Khouri, Wouter Vlemmings, Gunnar Nyman, Matthias Maercker, Ramlal Unnikrishnan, Behzad Bojnordi Arbab, Kirsten K. Knudsen, Susanne Aalto ยท 2025

Mass loss in oxygen-rich asymptotic giant branch (AGB) stars remains poorly understood, as the dust detected around them appears too transparent to drive winds through absorption alone. The current paโ€ฆ

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