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

AI-Accelerated Operator Learning Framework for Rarefied Microflows

Ehsan Roohi ยท 2025

The high computational cost of kinetic solvers such as DSMC remains a major challenge in rarefied flow simulations. This work presents a unified framework combining deep neural networks and neural opeโ€ฆ

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Physics Preprint PDF DOI

Machine learning-based prediction of magnet errors in storage ring light sources

Jianhao Xu ยท 2025

Magnet errors in storage rings significantly degrade beam performance, impacting the brightness and stability of the light source. Therefore, beam-based correction is crucial for the safe operation ofโ€ฆ

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Physics Preprint PDF DOI

Roadmap: 2D Materials for Quantum Technologies

Qimin Yan, Tongcang Li, Xingyu Gao, Sumukh Vaidya, Saakshi Dikshit, Yue Luo, Stefan Strauf, Reda Moukaouine, Anton Pershin, Adam Gali, Zhenyao Fang, Harvey Stanfield, Ivan J. Vera-Marun, Michael Newburger, Simranjeet Singh, Tiancong Zhu, Mauro Brotons-Gisbert, Klaus D. Jons, Brian D. Gerardot, Brian S. Y. Kim, John R. Schaibley, Kyle L. Seyler, Jesse Balgley, James Hone, Kin Chung Fong, Lin Wang, Guido Burkard, Yihang Zeng, Tobias Heindel, Serkan Ates, Tobias Vogl, Igor Aharonovich ยท 2025

Two-dimensional (2D) materials have emerged as a versatile and powerful platform for quantum technologies, offering atomic-scale control, strong quantum confinement, and seamless integration into heteโ€ฆ

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Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization

Shunyu Yin, Bernardo P. Ferreira, Gawel Kus, Miguel A. Bessa ยท 2025

Artificial neural networks accurately learn nonlinear, path-dependent material behavior. However, training them typically requires large, diverse datasets, often created via synthetic unit cell simulaโ€ฆ

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Physics Preprint PDF DOI

Composition-agnostic prediction of self-assembly in multicomponent amphiphile mixtures from molecular structure

Yuuki Ishiwatari, Takahiro Yokoyama, Tomoya Kojima, Taisuke Banno, Noriyoshi Arai ยท 2025

Predicting self-assembly in multi-component amphiphilic systems is challenging due to the complexity of intercomponent interactions and the combinatorial growth of possible formulations. In this studyโ€ฆ

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Machine Learning-Guided Discovery of Kagome Superconductors YRu3B2 and LuRu3B2

Rose Albu Mustaf, Sajilesh K. P., Sanu Mishra, Junze Deng, Yi Jiang, Kaja H. Hiorth, Eeli O. Lamponen, Martin Gutierrez-Amigo, Paivi Torma, Miguel A.L. Marques, B. Andrei Bernevig, Emilia Morosan ยท 2025

We report the experimental discovery of bulk superconductivity in two kagome lattice compounds, YRu$_3$B$_2$ and LuRu$_3$B$_2$, which were predicted through machine learning-accelerated high-throughpuโ€ฆ

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Physics Preprint PDF DOI

Hearing the light: stray-field noise from the emergent photon in quantum spin ice

Gautam K. Naik, Jonathan N. Hallen, Nishan C. Jayarama, Roderich Moessner, Chris R. Laumann ยท 2025

Decisive experimental confirmation of the $U(1)$ quantum spin liquid phase in quantum spin ice remains an outstanding challenge. In this work, we propose stray-field magnetometry as a direct probe of โ€ฆ

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Investigating the Efficacy of Topologically Derived Time Series for Flare Forecasting. II. XGBoost Model

Thomas Williams, Christopher B. Prior, David MacTaggart, D. Shaun Bloomfield ยท 2025

Solar flares are a primary driver of space weather, and forecasting their occurrence remains a significant challenge. This paper presents a novel flare prediction model based on topologically derived โ€ฆ

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Physics Preprint PDF DOI

Attention-Based Preprocessing Framework for Improving Rare Transient Classification

Xinyue Sheng, Tuan Dung Pham, Zichi Zhang, Matt Nicholl, Thai Son Mai ยท 2025

With large numbers of transients discovered by current and future imaging surveys, machine learning is increasingly applied to light curve and host galaxy properties to select events for follow-up. Hoโ€ฆ

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Hybrid Machine-Learning Particle Identification for the ePIC Proximity-Focusing RICH

D. H. Dongwi, C.-J. Naim, L. Rhode, A. Deshpande ยท 2025

We present a machine-learning-based particle-identification study for the proximity-focusing Ring Imaging Cherenkov (pfRICH) detector of the ePIC experiment at the Electron-Ion Collider. Operating in โ€ฆ

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Reading Qubits with Sequential Weak Measurements: Limits of Information Extraction

Cesar Lema, Aleix Bou-Comas, Atithi Acharya, Vadim Oganesyan, Anirvan Sengupta ยท 2025

Quantum information processing and computation requires high accuracy qubit configuration readout. In many practical schemes, the initial qubit configuration has to be inferred from readout that is a โ€ฆ

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Decoding Orbital Angular Momentum in Turbid Tissue-like Scattering Medium via Fourier-Domain Deep Learning

Avraham Yosovich, Anton Sdobnov, Alexander Doronin, Alexander Bykov, Igor Meglinski, Zeev Zalevsky ยท 2025

Structured light beams carrying orbital angular momentum (OAM), such as Laguerre-Gaussian modes, are promising tools for high-capacity optical communications and advanced biomedical imaging. However, โ€ฆ

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New Bulgarian-Austrian project 'Joint observations and investigations of solar chromospheric and coronal activity'

Rositsa Miteva, Werner Potzi, Astrid Veronig, Kamen Kozarev, Momchil Dechev, Robert Jarolim, Mohamed Nedal, Nikola Petrov, Stefan Purkhart, Christoph Schirninger, Tsvetan Tsvetkov, Yovelina Zinkova ยท 2025

We present the bilateral collaboration between Bulgarian and Austrian solar and space weather researchers on the topic of chromospheric and coronal activity. This new project will focus, on one hand, โ€ฆ

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Worldwide Scientific Landscape on Fires in Photovoltaic

Esther Salmeron-Manzano, David Munoz-Rodriguez, Alberto-Jesus Perea-Moreno, Quetzalcoatl Hernandez-Escobedo, Francisco Manzano-Agugliaro ยท 2025

The rapid growth of photovoltaic (PV) technology in recent years called for a comprehensive assessment of the global scientific landscape on fires associated with PV energy installations. This study eโ€ฆ

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The Thermal Unbalance Effect Induced by a Journal Bearing in Rigid and Flexible Rotors: Experimental Analysis

Thibaud Plantegenet (TriboLub), Mihai Arghir (TriboLub), Mohamed-Amine Hassini, Pascal Jolly (TriboLub) ยท 2025

The present work presents the experimental analyses of a rigid (short) and a flexible (long) rotor subject to thermal unbalance effects. The rotors are supported by a ball bearing and by a cylindricalโ€ฆ

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Quantum Machine Learning for Climate Modelling

Mierk Schwabe, Lorenzo Pastori, Valentina Sarandrea, Veronika Eyring ยท 2025

Quantum machine learning (QML) is making rapid progress, and QML-based models hold the promise of quantum advantages such as potentially higher expressivity and generalizability than their classical cโ€ฆ

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Complex Langevin simulations with a kernel

Michael Mandl, Erhard Seiler, Denes Sexty ยท 2025

We discuss recent developments regarding the use of kernels in complex Langevin simulations. In particular, we outline how a kernel can be used to solve the problem of wrong convergence in a simple toโ€ฆ

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Global fits and the search for new physics: past, present and future

Peter Athron, Csaba Balazs, Jon Butterworth, Christopher Chang, Andrew Fowlie, Tomas Gonzalo, Adil Jueid, Anders Kvellestad, Michele Lucente, Farvah Mahmoudi, Gregory D. Martinez, Are Raklev, Roberto Ruiz de Austri, Cristian Sierra, Wei Su, Aaron C. Vincent, Martin White, Lei Wu ยท 2025

In this work, we review the history and current role of global fits in the search for physics beyond the Standard Model~(BSM), including precision tests of the Standard Model (SM). Although BSM globalโ€ฆ

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Physics-Informed Machine Learning for Two-Phase Moving-Interface and Stefan Problems

Che-Chia Chang, Te-Sheng Lin, Ming-Chih Lai ยท 2025

The Stefan problem is a classical free-boundary problem that models phase-change processes and poses computational challenges due to its moving interface and nonlinear temperature-phase coupling. In tโ€ฆ

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Physics Preprint PDF DOI

Hierarchical Multi-agent Large Language Model Reasoning for Autonomous Functional Materials Discovery

Samuel Rothfarb, Megan C. Davis, Ivana Matanovic, Baikun Li, Edward F. Holby, Wilton J.M. Kort-Kamp ยท 2025

Artificial intelligence is reshaping scientific exploration, but most methods automate procedural tasks without engaging in scientific reasoning, limiting autonomy in discovery. We introduce Materialsโ€ฆ

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