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

Quantum Interior Point Methods: A Review of Developments and An Optimally Scaling Framework

Mohammadhossein Mohammadisiahroudi, Zeguan Wu, Pouya Sampourmahani, Adrian Harkness, Tamas Terlaky ยท 2025

The growing demand for solving large-scale, data-intensive linear and conic optimization problems, particularly in applications such as artificial intelligence and machine learning, has highlighted thโ€ฆ

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Physics Preprint PDF DOI

Comparative Analysis of Autonomous and Systematic Control Strategies for Hole-Doped Hubbard Clusters: Reinforcement Learning versus Physics-Guided Design

Shivanshu Dwivedi, Kalum Palandage ยท 2025

Engineering electron correlations in quantum dot arrays demands navigation of high-dimensional, non-convex parameter spaces where hole doping fundamentally alters the physics. We present a comparativeโ€ฆ

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Adsorption energies are necessary but not sufficient to identify good catalysts

Shahana Chatterjee, Alexander Davis, Lena Podina, Divya Sharma, Yoshua Bengio, Alexandre Duval, Oleksandr Voznyy, Alex Hernandez-Garcia, David Rolnick, Felix Therrien ยท 2025

As a core technology for green chemical synthesis and electrochemical energy storage, electrocatalysis is central to decarbonization strategies aimed at combating climate change. In this context, compโ€ฆ

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Physics Preprint PDF DOI

Learning the Cosmic Web: Graph-based Classification of Simulated Galaxies by their Dark Matter Environments

Dakshesh Kololgi, Krishna Naidoo, Amelie Saintonge, Ofer Lahav ยท 2025

We present a novel graph-based machine learning classifier for identifying the dark matter cosmic web environments of galaxies. Large galaxy surveys offer comprehensive statistical views of how galaxyโ€ฆ

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Physics Preprint PDF DOI

Euclid Quick Data Release (Q1). From simulations to sky: Advancing machine-learning lens detection with real Euclid data

Euclid Collaboration: N. E. P. Lines, T. E. Collett, P. Holloway, K. Rojas, S. Schuldt, R. B. Metcalf, T. Li, A. Verma, G. Despali, F. Courbin, R. Gavazzi, C. Tortora, B. Clement, N. Aghanim, B. Altieri, L. Amendola, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, A. Biviano, E. Branchini, M. Brescia, S. Camera, G. Canas-Herrera, V. Capobianco, C. Carbone, J. Carretero, M. Castellano, G. Castignani, S. Cavuoti, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, H. M. Courtois, M. Cropper, H. Degaudenzi, G. De Lucia, H. Dole, F. Dubath, X. Dupac, S. Dusini, A. Ealet, S. Escoffier, M. Farina, R. Farinelli, F. Faustini, S. Ferriol, F. Finelli, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, K. George, B. Gillis, C. Giocoli, P. Gomez-Alvarez, J. Gracia-Carpio, A. Grazian, F. Grupp, S. V. H. Haugan, W. Holmes, I. M. Hook, F. Hormuth, A. Hornstrup, K. Jahnke, M. Jhabvala, B. Joachimi, E. Keihanen, S. Kermiche, A. Kiessling, B. Kubik, M. Kummel, M. Kunz, H. Kurki-Suonio, A. M. C. Le Brun, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. J. Massey, E. Medinaceli, S. Mei, M. Melchior, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, S.-M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, C. Rosset, R. Saglia, Z. Sakr, A. G. Sanchez, D. Sapone, B. Sartoris, J. A. Schewtschenko, P. Schneider, T. Schrabback, A. Secroun, G. Seidel, S. Serrano, C. Sirignano, G. Sirri, L. Stanco, J. Steinwagner, P. Tallada-Crespi, A. N. Taylor, I. Tereno, N. Tessore, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, J. Valiviita, T. Vassallo, A. Veropalumbo, Y. Wang, J. Weller, A. Zacchei, G. Zamorani, F. M. Zerbi, E. Zucca, M. Ballardini, M. Bolzonella, E. Bozzo, C. Burigana, R. Cabanac, M. Calabrese, A. Cappi, T. Castro, J. A. Escartin Vigo, L. Gabarra, J. Garcia-Bellido, V. Gautard, S. Hemmati, M. Huertas-Company, J. Macias-Perez, R. Maoli, J. Martin-Fleitas, M. Maturi, N. Mauri, P. Monaco, M. Pontinen, C. Porciani, I. Risso, V. Scottez, M. Sereno, M. Tenti, M. Tucci, M. Viel, M. Wiesmann, Y. Akrami, I. T. Andika, G. Angora, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, E. Aubourg, L. Bazzanini, D. Bertacca, M. Bethermin, F. Beutler, A. Blanchard, L. Blot, M. Bonici, S. Borgani, M. L. Brown, S. Bruton, A. Calabro, B. Camacho Quevedo, F. Caro, C. S. Carvalho, F. Cogato, S. Conseil, A. R. Cooray, O. Cucciati, S. Davini, F. De Paolis, G. Desprez, A. Diaz-Sanchez, S. Di Domizio, J. M. Diego, P.-A. Duc, V. Duret, M. Y. Elkhashab, A. Enia, Y. Fang, P. G. Ferreira, A. Finoguenov, A. Fontana, A. Franco, K. Ganga, T. Gasparetto, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, A. Gruppuso, M. Guidi, C. M. Gutierrez, A. Hall, H. Hildebrandt, J. Hjorth, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, K. Kiiveri, J. Kim, C. C. Kirkpatrick, S. Kruk, M. Lattanzi, L. Legrand, F. Lepori, G. Leroy, G. F. Lesci, J. Lesgourgues, T. I. Liaudat, M. Magliocchetti, A. Manjon-Garcia, F. Mannucci, C. J. A. P. Martins, L. Maurin, M. Miluzio, A. Montoro, C. Moretti, G. Morgante, S. Nadathur, K. Naidoo, P. Natoli, S. Nesseris, D. Paoletti, F. Passalacqua, K. Paterson, L. Patrizii, A. Pisani, D. Potter, G. W. Pratt, S. Quai, M. Radovich, W. Roster, S. Sacquegna, M. Sahlen, D. B. Sanders, E. Sarpa, A. Schneider, D. Sciotti, E. Sellentin, L. C. Smith, J. G. Sorce, K. Tanidis, C. Tao, F. Tarsitano, G. Testera, R. Teyssier, S. Tosi, A. Troja, A. Venhola, D. Vergani, G. Vernardos, G. Verza, S. Vinciguerra, M. Walmsley, N. A. Walton, A. H. Wright ยท 2025

In the era of large-scale surveys like Euclid, machine learning has become an essential tool for identifying rare yet scientifically valuable objects, such as strong gravitational lenses. However, supโ€ฆ

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Physics Preprint PDF DOI

A Machine Learning Framework for Predicting Glass-Forming Ability in Ternary Alloy Systems

Fatemeh Mahmoudi ยท 2025

Predicting the glass-forming ability (GFA) of chemical compositions remains a fundamental challenge in materials science, especially for oxide glasses with broad compositional diversity. Traditional eโ€ฆ

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Machine-learning-enabled interpretation of tribological deformation patterns in large-scale MD data

Hendrik J. Ehrich, Marvin C. May, Stefan J. Eder ยท 2025

Molecular dynamics (MD) simulations have become indispensable for exploring tribological deformation patterns at the atomic scale. However, transforming the resulting high-dimensional data into interpโ€ฆ

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Investigating all-sky Frequency Hough performances for neutron stars

Martina Di Cesare, Pia Astone, Rosario De Rosa, David Keitel, Cristiano Palomba, Marco Serra ยท 2025

Between the estimated population of Neutron Stars (NSs) and the actual number present in the catalogs, there is a huge gap: O(10$^{8-9}$) vs O(10$^3$). Among the different search techniques for Continโ€ฆ

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Physics Preprint PDF DOI

Machine Learning-Informed 3+1 Sterile Neutrino Global Fits using Posterior Density Estimation of Electron Disappearance Data

Joshua Villarreal, Julia Woodward, John Hardin, Janet Conrad ยท 2025

Global analyses of particle physics data are integral for validating and scrutinizing published results of experiments. Global fits of anomalous oscillation data which search for one or more eV-scale โ€ฆ

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Physics Preprint PDF DOI

Stochastic Reconfiguration with Warm-Started SVD

Dexuan Zhou, Huajie Chen, Cheuk Hin Ho, Xin Liu, Christoph Ortner ยท 2025

The combination of the variational Monte Carlo (VMC) method with deep learning wave function architectures has led to several successes in ground-state calculations of quantum many-body systems in recโ€ฆ

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A High-Order Immersed Boundary Method for Fluid-Structure Interaction Problems

Yingjie Xia, Stefano Colombo, David Huergo, Jiaqing Kou, Yuting Dai, Esteban Ferrer ยท 2025

Accurate and efficient simulation of fluid-structure interaction (FSI) problems remains a central challenge in computational physics. High-order discontinuous Galerkin (DG) methods offer low numericalโ€ฆ

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The tube transducer as a novel source for power ultrasound: A case study in delamination of graphite coating from lithium-ion battery anode

Shida Li, Paul Daly, Ben Jacobson, Joshua Cooke, Chunhong Lei, Andrew P. Abbott, Andrew Feeney, Paul Prentice ยท 2025

Developing high throughput applications of sonochemistry and sonoprocessing is an outstanding ultrasonic engineering challenge that continues to limit widespread industrial adoption. Conventional massโ€ฆ

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The Ratan Active Region Patches (RARPs) Database: A New Database of Solar Active Region Radio Signatures from the RATAN-600 Telescope

Maxim Korelov, Irina Knyazeva, Evgenii Kurochkin, Nikolay Makarenko, Denis Derkach ยท 2025

Solar flares and coronal mass ejections, originating from solar active regions (ARs), are the primary drivers of space weather and can disrupt technological systems. Forecasting efforts heavily rely oโ€ฆ

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Revealing interstitial energetics in Ti-23Nb-0.7Ta-2Zr gum metal base alloy via universal machine learning interatomic potentials

Miroslav Lebeda, Jan Drahokoupil, Veronika Mazacova, Petr Vlcak ยท 2025

Understanding the behavior of light interstitial elements in multicomponent alloys remains challenging due to the complexity of local chemical environments and the high computational cost of first-priโ€ฆ

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Machine and Deep Learning Regression for Compact Object Equations of State

I. Stergakis, Th. Diakonidis, Ch.C. Moustakidis ยท 2025

A central open problem in nuclear physics is the determination of a physically robust equation of state (EoS) for dense nuclear matter, which directly informs our understanding of the internal composiโ€ฆ

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Guest metal-driven quantum anharmonic effects on stability and two-gap superconductivity in carbon-boron clathrates

Xianghui Meng, Yanqing Shen, Xin Yang, Xinyu Wang, Qing Ai, Yong Shuai, Zhongxiang Zhou ยท 2025

Traditionally, strong quantum anharmonic effects have been considered a characteristic of hydrogen-rich compounds. Here we propose that these effects also play a decisive role in boron-carbon clathratโ€ฆ

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Awakening catalytically active surface of BaRuO3 thin film for alkaline hydrogen evolution

Jegon Lee, Dohyun Kim, Seulgi Ji, Sangmoon Yoon, Seung Hyun Nam, Jucheol Park, Jin Young Oh, Seung Gyo Jeong, Jong-Seong Bae, Sang A Lee, Heechae Choi, Woo Seok Choi ยท 2025

The dynamic reconstruction of surfaces during electrochemical reactions plays a crucial role in determining the performance of electrocatalysts. However, because reconstructions occur at the atomic leโ€ฆ

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Platonic representation of foundation machine learning interatomic potentials

Zhenzhu Li, Aron Walsh ยท 2025

Foundation machine learning interatomic potentials (MLIPs) are trained on overlapping chemical spaces, yet their latent representations remain model-specific. Here, we show that independently developeโ€ฆ

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Hypothesis-Based Particle Detection for Accurate Nanoparticle Counting and Digital Diagnostics

Neil H. Kim, Xiao-Liu Chu, Joseph B. DeGrandchamp, Matthew R. Foreman ยท 2025

Digital assays represent a shift from traditional diagnostics and enable the precise detection of low-abundance analytes, critical for early disease diagnosis and personalized medicine, through discreโ€ฆ

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Benchmarking Universal Machine Learning Interatomic Potentials for Supported Nanoparticles: Decoupling Energy Accuracy from Structural Exploration

Jiayan Xu, Abhirup Patra, Amar Deep Pathak, Sharan Shetty, Detlef Hohl, Roberto Car ยท 2025

Supported nanoparticle catalysts are widely used in the chemical industry. Computational modeling of supported nanoparticles based on density functional theory (DFT) often involves structural searchesโ€ฆ

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