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

Hierarchy of extreme-event predictability in turbulence revealed by machine learning

Yuxuan Yang, Chenyu Dong, Gianmarco Mengaldo ยท 2026

Extreme-event predictability in turbulence is strongly state dependent, yet event-by-event predictability horizons are difficult to quantify without access to governing equations or costly perturbatioโ€ฆ

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Physics Preprint PDF DOI

Research Paradigm of Materials Science Tetrahedra with Artificial Intelligence

Shiyun Zhang, Yibo Yao, Haoquan Long, Dingwen Tao, Guangming Tan, Wei-Hua Wang, Yuan-Chao Hu ยท 2026

The classical material tetrahedron that represents the Structure-Property-Processing-Performance-Characterization relationship is the most important research paradigm in materials science so far. It hโ€ฆ

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Physics Preprint PDF DOI

history: A tool for fully-differential cross sections at next-to-next-to-leading order

Sven Yannick Klein, Lukas Simon ยท 2026

The software $\texttt{history}$ is designed to calculate fully-differential cross sections for colour-singlet production processes in hadronic collision up to next-to-next-to-leading order in QCD. It โ€ฆ

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Physics Preprint PDF DOI

AI assisted optimization of integrated waveguide polarizers containing 2D reduced graphene oxide

Rong Wang, Yijun Wang, Di Jin, Junkai Hu, Wenbo Liu, Yuning Zhang, Duan Huang, Jiayang Wu, Baohua Jia, David J. Moss ยท 2026

Reduced graphene oxide (rGO) exhibits strong anisotropic light absorption and high compatibility with photonic integrated chips, making it a promising material for implementing high performance onchipโ€ฆ

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Physics Preprint PDF DOI

Active Sampling Sample-based Quantum Diagonalization from Finite-Shot Measurements

Rinka Miura ยท 2026

Near-term quantum devices provide only finite-shot measurements and prepare imperfect, contaminated states. This motivates algorithms that convert samples into reliable low-energy estimates without fuโ€ฆ

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Physics Preprint PDF DOI

A Versatile Laboratory Approach to Reproduce and Analyze Internal Ocean Wave Dynamics

Vohn Jacquez, Zachary Phan, Zachary Taebel, Dylan Brunei, Pierre-Yves Passaggia, Alberto Scotti ยท 2026

Internal waves, or waves that propagate within a stratified fluid, may break and cause mixing. While each individual mixing event may be small, collectively, internal wave breaking drive processes in โ€ฆ

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Physics Preprint PDF DOI

Ultra Fast Calorimeter Simulation with Generative Machine Learning on FPGAs

P. Alex May, Qibin Liu, Julia Gonski, Benjamin Nachman ยท 2026

Computationally expensive, high-accuracy detector simulations are a major bottleneck for many particle physics experiments such as those at the Large Hadron Collider (LHC) as well as those planned forโ€ฆ

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Physics Preprint PDF DOI

Distance learning from projective measurements as an information-geometric probe of many-body physics

Oleksii Malyshev, Simon M. Linsel, Fabian Grusdt, Annabelle Bohrdt, Eugene Demler, Ivan Morera ยท 2026

The ability of modern quantum simulators--both digital and analogue--to generate large ensembles of single-shot projective "snapshots" has opened a data-rich avenue for the study of quantum many-body โ€ฆ

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Physics Preprint PDF DOI

From Experiments to Expertise: Scientific Knowledge Consolidation for AI-Driven Computational Research

Haonan Huang ยท 2026

While large language models (LLMs) have transformed AI agents into proficient executors of computational materials science, performing a hundred simulations does not make a researcher. What distinguisโ€ฆ

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Physics Preprint PDF DOI

Parameter adjustment of nuclear leading-order local pairing energy density functionals

Michael Bender, Karim Bennaceur, Valentin Guillon ยท 2026

(See paper for full abstract) This study reports on the benchmarking of a protocol for the adjustment of the parameters of a local leading-order (LO) T=1 (like-particle) pairing EDF that consists inโ€ฆ

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Physics Preprint PDF DOI

Data-efficient surrogate modeling of spectral functions using Gaussian processes: An application to the $t$-$t'$-$t''$-$J$ model

Sanket Jantre, Nathan M. Urban, Weiguo Yin, Niraj Aryal ยท 2026

Spectral functions encode key many-body information but are costly to compute with high fidelity. Machine-learning surrogates have emerged as a powerful alternative, yet many approaches require large โ€ฆ

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Noise mitigation of quantum observables via learning from Hamiltonian symmetry decays

Javier Oliva del Moral, Olatz Sanz Larrarte, Joana Fraxanet, Dmytro Mishagli, Josu Etxezarreta Martinez ยท 2026

We present a new quantum error mitigation technique (QEM), called GUiding Extrapolations from Symmetry decayS (GUESS), which exploits Hamiltonian symmetries to improve accuracy of noisy quantum computโ€ฆ

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Breaking concentration barriers for quantum extreme learning on digital quantum processors

Timothee Dao, Ege Yilmaz, Ibrahim Shehzad, Christophe Pere, Kumar Ghosh, Isabelle Wittmann, Thomas Brunschwiler, Giorgio Cortiana, Corey O'Meara, Stefan Woerner, Francesco Tacchino ยท 2026

Reservoir computing leverages rich, non-linear dynamics to process temporal data. Quantum variants promise enhanced expressivity from high-dimensional Hilbert spaces, yet their practical applicabilityโ€ฆ

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Diffusion-based Generative Machine Learning Model for Predicting Crack Propagation in Aluminum Nitride at the Atomic Scale

Jiali Lu, Shengfeng Yang ยท 2026

Predicting atomic-scale crack propagation in aluminum nitride (AlN) is critical for semiconductor reliability but remains prohibitively expensive via molecular dynamics (MD). We develop a diffusion-baโ€ฆ

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Large dilatational hyperelasticity of glasses en route to cavitation failure

Pawandeep Kaur, Noam Ottolenghi, Edan Lerner, David Richard, Eran Bouchbinder ยท 2026

Materials deform elasto-plastically and fail under various loading conditions, typically quantified by the stress triaxiality, which is the ratio between the dilatational (hydrostatic) stress and the โ€ฆ

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Quantifying Perovskite Solar Cell Degradation via Machine Learning from Spatially Resolved Multimodal Luminescence Time Series

Giulio Barletta, Simon Ternes, Saif Ali, Zohair Abbas, Chiara Ostendi, Marialucia D'Addio, Erica Magliano, Pietro Asinari, Eliodoro Chiavazzo, Aldo Di Carlo ยท 2026

Perovskite solar cells (PSCs) have experienced a remarkable rise in power conversion efficiency (PCE) over the past 15 years, positioning them as a promising alternative or complement to silicon for lโ€ฆ

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Physics Preprint PDF DOI

Deep-Learning-Designed AlGaAs Interface Linking Trapped Ions to Telecom Quantum Networks

I.P. De Simeone, G. Maltese, V. Cambier, J-P. Likforman, M. Ravaro, L. Guidoni, F. Baboux, S. Ducci ยท 2026

The realization of a scalable quantum internet requires efficient light-matter interfaces that map stationary qubits onto photonic carriers for long-distance transmission. A central challenge is the gโ€ฆ

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Gaia GraL X.: The GraL catalogue of gravitationally lensed quasars Matched with \textit{Gaia} data, redshifts, and time delays

C. Ducourant, R. Teixeira, P. H. Vale-Cunha, L. Delchambre, A. Krone-Martins, J. Braine, L. Galluccio, J-F. Le Campion, O. S. Krinski-Moreira, S. Scarano Jr, C. Boehm, T. Connor, S. G. Djorgovski, M. J. Graham, P. Jalan, Q. Petit, S. A. Klioner, F. Mignard, V. Negi, J. Sebastian den Brok, I. Slezak, E. Slezak, C. Spindola-Duarte, D. Stern, J. Surdej, D. Sweeney, D. J. Walton, J. Wambsganss ยท 2026

Determining the Hubble constant tension requires alternative strategies, and multiply imaged quasars, with their intermediate redshifts, can potentially be used in this regard. We provide a currently โ€ฆ

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Physics Preprint PDF DOI

Advancing Machine Learning Applications in Quantum Few-Body Systems

Jin Ziqi, Paolo Recchia, Mario Gattobigio ยท 2026

This paper presents a general neural network framework for solving quantum few-body systems, extending prior methods to handle diverse particle masses, interaction types, and system configurations. Ouโ€ฆ

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Physics Preprint PDF DOI

Adversarial Stress Tests for Quantum Certification

Veronica Sanz, Augusto Smerzi ยท 2026

We develop a practical framework for semi-device-independent (SDI) certification under operational deviations from the ideal protocol model. Apparent violations of classical benchmarks need not signalโ€ฆ

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