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

Crystal structure prediction with nuclear quantum and finite-temperature effects via deep free energy learning

Xiaoyang Wang, Yinan Wang, Wenbo Zhao, Hanyu Liu, Hao Xie, Lei Wang, Han Wang ยท 2026

Accurate crystal structure prediction (CSP) requires accounting for finite-temperature and nuclear quantum effects, yet first-principles evaluation of the free energy surface (FES) remains prohibitiveโ€ฆ

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Physics Preprint PDF DOI

Autonomous operation of the DIAG0 diagnostic line for 6D phase-space monitoring at LCLS-II

Ryan Roussel, Gopika Bhardwaj, Dylan Kennedy, Chris Garnier, An Le, William Colocho, Michael Ehrlichman, Yuantao Ding, Feng Zhou, Auralee Edelen ยท 2026

Characterizing the full 6-dimensional phase-space distribution of beams from the LCLS-II photoinjector is essential for understanding and optimizing downstream accelerator performance. Long-term monitโ€ฆ

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Physics Preprint PDF DOI

A Physics-Informed Neural Network for Solving the Quasi-static Magnetohydrodynamic Equations

Jonathan S. Arnaud, Christopher J. McDevitt, Golo Wimmer, Xian-Zhu Tang ยท 2026

A physics-informed neural network (PINN) is developed, for the first time, to learn the time-dependent quasi-static magnetohydrodynamic (MHD) equations in axisymmetric tokamak geometry, without any exโ€ฆ

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Physics Preprint PDF DOI

Predictivity and Utility of Neural Surrogates of Multiscale PDEs

Karthik Duraisamy ยท 2026

Scientific machine learning is increasingly being spoken of as universal emulators for classical numerical solvers for multi-scale partial differential equations, but most apparent successes can be exโ€ฆ

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Physics Preprint PDF DOI

Reinforcement Learning for Robust Calibration of Multi-Qudit Quantum Gates

Amine Jaouadi, Sahel Ashhab ยท 2026

Higher-dimensional quantum systems, such as qudits, offer architectural and algorithmic advantages over qubits, but their increased spectral crowding and limited controllability render high-fidelity qโ€ฆ

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Physics Preprint PDF DOI

Accelerating the Design of Resorbable Magnesium Alloys: A Machine Learning Approach to Property Prediction

Vickey Nandal, Vit Benes, Pavel Balaz, Jiri Ryjacek, Karel Tesar ยท 2026

Resorbable magnesium (Mg) alloys are promising candidates for temporary medical devices due to their biodegradability and favorable mechanical properties. To accelerate the design of diluted Mg alloysโ€ฆ

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Physics Preprint PDF DOI

Is the `Known' Enough? An Integrated Machine Learning Framework for Eclipsing Binary Classification and Parameter Estimation Based on Well-Characterized Systems

Burak Ulas ยท 2026

This study presents a multi-task machine learning framework for simultaneous morphology classification and physical parameter estimation of eclipsing binaries using photometric light curves. We train โ€ฆ

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Physics Preprint PDF DOI

Predicting Scale-Up of Metal-Organic Framework Syntheses with Large Language Models

Peter Walther, Hongrui Sheng, Xinxin Liu, Bin Feng, Reid Coyle, Xinhua Yan, Kyle Smith, Harrison Kayal, Shyam Chand Pal, Zhiling Zheng ยท 2026

Scalable synthesis remains the gate between MOF discovery and industrial deployment, as scale-up know-how is fragmented across disparate reports. We introduce ESU-MOF, a literature-mined dataset and aโ€ฆ

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Physics Preprint PDF DOI

Competing Constraints on Superconductivity in Thick FeSe films

Ya-Xun He, Xing-Jian Liu, Qun Wang, Ting Chen, Hassan Ali, Jia-Ying Zhang, Bao-Juan Kang, Zheng Zhang, Jun-Yi Ge ยท 2026

Superconducting films emerge from the complex interplay of multiple growth parameters, making their optimization challenging. In iron-based superconductors, compressive strain is known to enhance the โ€ฆ

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Physics Preprint PDF DOI

Next-to-leading order QCD and relativistic corrections to $Z \to J/\psi+\Upsilon(nS)$

Guang-Yu Wang, Xu-Chang Zheng, Guang-Zhi Xu ยท 2026

In this paper, we calculate the decay widths and branching fractions for the decays $Z \to J/\psi+\Upsilon(nS)$ ($n=1,2,3$) at future super $Z$ factory and at the CEPC/FCC-ee, including both the relatโ€ฆ

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Physics Preprint PDF DOI

Neural posterior estimation of the neutrino direction in IceCube using transformer-encoded normalizing flows on the sphere

R. Abbasi, M. Ackermann, J. Adams, J. A. Aguilar, M. Ahlers, J.M. Alameddine, S. Ali, N. M. Amin, K. Andeen, C. Arguelles, Y. Ashida, S. Athanasiadou, S. N. Axani, R. Babu, X. Bai, A. Balagopal V., S. W. Barwick, V. Basu, R. Bay, J. J. Beatty, J. Becker Tjus, P. Behrens, J. Beise, C. Bellenghi, S. Benkel, S. BenZvi, D. Berley, E. Bernardini, D. Z. Besson, E. Blaufuss, L. Bloom, S. Blot, F. Bontempo, J. Y. Book Motzkin, C. Boscolo Meneguolo, S. Boser, O. Botner, J. Bottcher, J. Braun, B. Brinson, Z. Brisson-Tsavoussis, R. T. Burley, D. Butterfield, K. Carloni, J. Carpio, N. Chau, Z. Chen, D. Chirkin, S. Choi, A. Chubarov, B. A. Clark, G. H. Collin, D. A. Coloma Borja, A. Connolly, J. M. Conrad, D. F. Cowen, C. De Clercq, J. J. DeLaunay, D. Delgado, T. Delmeulle, S. Deng, P. Desiati, K. D. de Vries, G. de Wasseige, T. DeYoung, J. C. Diaz-Velez, S. DiKerby, T. Ding, M. Dittmer, A. Domi, L. Draper, L. Dueser, D. Durnford, K. Dutta, M. A. DuVernois, T. Ehrhardt, L. Eidenschink, A. Eimer, C. Eldridge, P. Eller, E. Ellinger, D. Elsasser, R. Engel, H. Erpenbeck, W. Esmail, S. Eulig, J. Evans, P. A. Evenson, K. L. Fan, K. Fang, K. Farrag, A. R. Fazely, A. Fedynitch, N. Feigl, C. Finley, D. Fox, A. Franckowiak, S. Fukami, P. Furst, J. Gallagher, E. Ganster, A. Garcia, M. Garcia, E. Genton, L. Gerhardt, A. Ghadimi, C. Glaser, T. Glusenkamp, J. G. Gonzalez, S. Goswami, A. Granados, D. Grant, S. J. Gray, S. Griffin, K. M. Groth, D. Guevel, C. Gunther, P. Gutjahr, C. Ha, A. Hallgren, L. Halve, F. Halzen, L. Hamacher, M. Handt, K. Hanson, J. Hardin, A. A. Harnisch, P. Hatch, A. Haungs, J. Hau{ss}ler, K. Helbing, J. Hellrung, B. Henke, L. Hennig, F. Henningsen, L. Heuermann, R. Hewett, N. Heyer, S. Hickford, A. Hidvegi, C. Hill, G. C. Hill, R. Hmaid, K. D. Hoffman, A. Hollnagel, D. Hooper, S. Hori, K. Hoshina, M. Hostert, W. Hou, M. Hrywniak, T. Huber, K. Hultqvist, K. Hymon, A. Ishihara, W. Iwakiri, M. Jacquart, S. Jain, O. Janik, M. Jansson, M. Jin, N. Kamp, D. Kang, W. Kang, A. Kappes, L. Kardum, T. Karg, A. Karle, A. Katil, M. Kauer, J. L. Kelley, M. Khanal, A. Khatee Zathul, A. Kheirandish, T. Kim, H. Kimku, F. Kirchner, J. Kiryluk, C. Klein, S. R. Klein, Y. Kobayashi, S. Koch, A. Kochocki, R. Koirala, H. Kolanoski, T. Kontrimas, L. Kopke, C. Kopper, D. J. Koskinen, P. Koundal, M. Kowalski, T. Kozynets, A. Kravka, N. Krieger, T. Krishnan, K. Kruiswijk, E. Krupczak, A. Kumar, E. Kun, N. Kurahashi, C. Lagunas Gualda, L. Lallement Arnaud, M. J. Larson, F. Lauber, J. P. Lazar, K. Leonard DeHolton, A. Leszczynska, C. Li, J. Liao, C. Lin, Q. R. Liu, Y. T. Liu, M. Liubarska, C. Love, L. Lu, F. Lucarelli, W. Luszczak, Y. Lyu, M. Macdonald, E. Magnus, Y. Makino, E. Manao, S. Mancina, A. Mand, I. C. Maris, S. Marka, Z. Marka, L. Marten, I. Martinez-Soler, R. Maruyama, J. Mauro, F. Mayhew, F. McNally, K. Meagher, A. Medina, M. Meier, Y. Merckx, L. Merten, J. Mitchell, L. Molchany, S. Mondal, T. Montaruli, R. W. Moore, Y. Morii, A. Mosbrugger, D. Mousadi, E. Moyaux, T. Mukherjee, M. Nakos, U. Naumann, J. Necker, L. Neste, M. Neumann, H. Niederhausen, M. U. Nisa, K. Noda, A. Noell, A. Novikov, A. Obertacke, V. O'Dell, A. Olivas, R. Orsoe, J. Osborn, E. O'Sullivan, B. Owens, V. Palusova, H. Pandya, A. Parenti, N. Park, V. Parrish, E. N. Paudel, L. Paul, C. Perez de los Heros, T. Pernice, T. C. Petersen, J. Peterson, S. Pick, M. Plum, A. Ponten, V. Poojyam, B. Pries, R. Procter-Murphy, G. T. Przybylski, L. Pyras, C. Raab, J. Rack-Helleis, N. Rad, M. Ravn, K. Rawlins, Z. Rechav, A. Rehman, I. Reistroffer, E. Resconi, S. Reusch, C. D. Rho, W. Rhode, L. Ricca, B. Riedel, A. Rifaie, E. J. Roberts, S. Rodan, M. Rongen, A. Rosted, C. Rott, T. Ruhe, L. Ruohan, D. Ryckbosch, J. Saffer, D. Salazar-Gallegos, P. Sampathkumar, A. Sandrock, G. Sanger-Johnson, M. Santander, S. Sarkar, M. Scarnera, M. Schaufel, H. Schieler, S. Schindler, L. Schlickmann, B. Schluter, F. Schluter, N. Schmeisser, T. Schmidt, A. Scholz, F. G. Schroder, S. Schwirn, S. Sclafani, D. Seckel, L. Seen, M. Seikh, S. Seunarine, P. A. Sevle Myhr, R. Shah, S. Shah, S. Shefali, N. Shimizu, B. Skrzypek, R. Snihur, J. Soedingrekso, D. Soldin, P. Soldin, G. Sommani, C. Spannfellner, G. M. Spiczak, C. Spiering, J. Stachurska, M. Stamatikos, T. Stanev, T. Stezelberger, T. Sturwald, T. Stuttard, G. W. Sullivan, I. Taboada, S. Ter-Antonyan, A. Terliuk, A. Thakuri, M. Thiesmeyer, W. G. Thompson, J. Thwaites, S. Tilav, K. Tollefson, J. A. Torres, S. Toscano, D. Tosi, K. Upshaw, A. Vaidyanathan, N. Valtonen-Mattila, J. Valverde, J. Vandenbroucke, T. Van Eeden, N. van Eijndhoven, L. Van Rootselaar, J. van Santen, J. Vara, F. Varsi, M. Venugopal, M. Vereecken, S. Vergara Carrasco, S. Verpoest, D. Veske, A. Vijai, J. Villarreal, C. Walck, A. Wang, E. H. S. Warrick, C. Weaver, P. Weigel, A. Weindl, J. Weldert, A. Y. Wen, C. Wendt, J. Werthebach, M. Weyrauch, N. Whitehorn, C. H. Wiebusch, D. R. Williams, L. Witthaus, G. Wrede, X. W. Xu, J. P. Yanez, Y. Yao, E. Yildizci, S. Yoshida, R. Young, F. Yu, S. Yu, T. Yuan, S. Yun-Carcamo, A. Zander Jurowitzki, A. Zegarelli, S. Zhang, Z. Zhang, P. Zhelnin, P. Zilberman ยท 2026

IceCube is a cubic-kilometer-scale neutrino detector located at the geographic South Pole. A precise directional reconstruction of IceCube neutrinos is vital for associations with astronomical objectsโ€ฆ

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Physics Preprint PDF DOI

Multimodal Transformer for Sample-Aware Prediction of Metal-Organic Framework Properties

Seunghee Han, Jaewoong Lee, Jihan Kim ยท 2026

Metal-organic frameworks (MOFs) are a major target of machine-learning-based property prediction, yet most models assume that a single framework representation maps to a single property value. This asโ€ฆ

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Experimental Demonstration of SDRL Controller for TS Wave Suppression with DBD Actuator

Babak Mohammadikalakoo, Sergio Garcia Villasol, Gabriele Salomone, Marios Kotsonis, Nguyen Anh Khoa Doan ยท 2026

An experimental wind-tunnel implementation of a model-free single-step deep reinforcement learning (SDRL) controller is presented for TS wave suppression in a flat plate boundary layer. The controllerโ€ฆ

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Physics Preprint PDF DOI

Daydreaming algorithm for Biased Patterns

Mikiya Doi, Masayuki Ohzeki, Federico Ricci-Tersenghi ยท 2026

The \emph{Daydreaming} algorithm was proposed as a learning rule that simultaneously reinforces stored patterns and suppresses spurious attractors to improve the storage capacity of the Hopfield modelโ€ฆ

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Physics Preprint PDF DOI

Deep-Learning based surrogate models for plasma exhaust simulations -- SOLPS-NN

Stefan Dasbach, Sebastijan Brezinsek, Yunfeng Liang, Dirk Reiser, Sven Wiesen ยท 2026

Accurate models of the scrape-off layer are required for the design and operation of tokamak fusion reactors. Scrape-off layer simulations are computationally expensive, difficult to operate and suffeโ€ฆ

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Identifying Merger-Driven and Collapsar-Driven Gamma-Ray Bursts with Precursor based Solely on Prompt Emission

Si-Yuan Zhu, Pak-Hin Thomas Tam, Fu-Wen Zhang, Hui-Ying Deng, Bing Zhang ยท 2026

Gamma-ray bursts (GRBs) are generally classified as Type~I GRBs, which originate from compact binary mergers, and Type~II GRBs, which originate from massive collapsars. The traditional correspondence โ€ฆ

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Physics Preprint PDF DOI

Closing the Loop: Deploying Auto-Generating Digital Twins for Particle Accelerators

A. D. Brynes, M. King, K. R. L. Baker, R. Banerjee, R. Clarke, D. J. Dunning, J. K. Jones, M. Leputa, A. E. Pollard, M. Romanovschi, M. Shaw, N. Ziyan ยท 2026

The simulation of a physical system in a virtual replica, known as a digital twin, is a useful way to interrogate the system non-invasively, providing the ability to perform predictive maintenance andโ€ฆ

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Discovery of Graphene Sheets and C-Rich Micro-Oval structure in Stingless Bee Hive; Leading to an Emergent Material with Debut of Blue Emission

Manas Kumar Dalai, Ankita Mahakhuda, Abinash Prusty ยท 2026

Naturally produced stingless bee hive (NP-SBH) is an intricately produced material by the combination of waxes, resin and other biological materials that offers protection and structural stability to โ€ฆ

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Towards Automated Selection of Quantum Encoding Circuits via Meta-Learning

Dao Duy Tung, Nguyen Quoc Chuong, Vu Tuan Hai, Le Bin Ho, Lan Nguyen Tran ยท 2026

In recent years, quantum kernel methods have shown promising applications on near-term quantum devices. However, selecting an appropriate encoding circuit for a given dataset requires costly evaluatioโ€ฆ

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Three-dimensional recoil-electron reconstruction using combined optical imaging and waveform readout for electron-tracking Compton cameras

Tomonori Ikeda, Tatsuya Sawano, Naomi Tsuji, Yoshitaka Mizumura ยท 2026

Accurate reconstruction of recoil-electron directions is critical for enhancing the point-spread function of electron-tracking Compton cameras (ETCCs) in gamma-ray imaging. Although full three-dimensiโ€ฆ

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