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Showing 45524 results for "machine learning" in Physics
Physics Preprint PDF DOI

Defending Quantum Classifiers against Adversarial Perturbations through Quantum Autoencoders

Emma Andrews, Sahan Sanjaya, Prabhat Mishra ยท 2026

Machine learning models can learn from data samples to carry out various tasks efficiently. When data samples are adversarially manipulated, such as by insertion of carefully crafted noise, it can cauโ€ฆ

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Mapping the Phase Diagram of the Vicsek Model with Machine Learning

Grace T. Bai, Brandon B. Le ยท 2026

In this study, we use machine learning to classify and interpolate the phase structure of the Vicsek flocking model across the three-dimensional parameter space $(\eta,\rho,v_0)$. We construct a datasโ€ฆ

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Reorganizing Quantum Measurement Records Improves Time-Series Prediction

Markus Baumann, Maximilian Zorn, Thomas Gabor, Claudia Linnhoff-Popien, Jonas Stein ยท 2026

Near-term quantum computers are accessed through repeated circuit executions, which produce finite measurement records rather than exact deterministic outputs. In quantum reservoir computing, these reโ€ฆ

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Physics Preprint PDF DOI

Machine Learning and Molecular Simulations Reveal Mechanisms of ZIFs Polymorph Selection

Emilio Mendez, Rocio Semino ยท 2026

Zn(imidazolate)$_2$ metal-organic frameworks (MOFs) exhibit a remarkable degree of polymorphism. Because of their promising industrial applications, many research groups have investigated phase transiโ€ฆ

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Physics Preprint PDF DOI

Many-mode grating couplers by avoiding undesired couplings

Nazar Pyvovar, Hao Li, Zhaowei Dai, Owen D. Miller ยท 2026

To couple many independent modes from free space to on chip, the key challenge is not enhancing the many necessary coupling rates (scattering-matrix elements) between targeted mode pairs. Instead, theโ€ฆ

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Physics Preprint PDF DOI

Learning quantum disentanglement scheduling from reduced states via modular hybrid policies

Y.-X. Xiao, J.-Z. Han, Z. Zheng, Z.-H. Zhang, M. Xue, J. Li, X. Lv ยท 2026

Quantum control with restricted state access is central to near-term quantum devices, where full wave-function information is unavailable. We study this problem through multiqubit disentanglement scheโ€ฆ

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Physics Preprint PDF DOI

Propelling catalytic structures using active phase separation

Benjamin Sorkin, Ned S. Wingreen ยท 2026

Living systems routinely consume energy to achieve motility, often using intricate biomolecular machinery. In this work, we show that active droplets can sustain indefinite self-propulsion of a spheriโ€ฆ

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Physics Preprint PDF DOI

The Large Array Survey Telescope-Pipeline. II. Image Subtraction and Transient Detection

R. Konno, E. O. Ofek, A. Krassilchtchikov, Y. Shvartzvald, S. Ben-Ami, D. Polishook, C. Tishler, E. Segre, S. Garrappa, E. A. Zimmermann, A. Horowicz, P. Chen, A. Gal-Yam, M. Engel, Y. M. Shani, S. A. Spitzer, S. Fainer, O. Yaron, A. Blumenzweig ยท 2026

Context. The Large Array Survey Telescope (LAST) is a wide-field visual-band survey designed to explore the variable and transient sky with high cadence. Its raw data stream is automatically processedโ€ฆ

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Physics Preprint PDF DOI

Fragment-Constrained Charge Equilibration for Charge-Aware Machine Learning Potentials at Electrochemical Interfaces

Akhil Reddy Peeketi, Blas P Uberuaga, Travis E Jones ยท 2026

Predictive simulation of electrochemical interfaces requires atomistic models that capture reactive bond rearrangements, long-range electrostatics, and charge distributions reflecting the electronic dโ€ฆ

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Physics Preprint PDF DOI

Generation of magnetic metal-organic frameworks

Alexander C. Tyner, Avinash Pathapati, Alexander V. Balatsky ยท 2026

The potential to utilize metal-organic frameworks as a replacement for rare earth materials as well as in technological applications has prompted increased interested in this material class. The simulโ€ฆ

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Acoustic modulation of shear thickening transition in dense adhesive suspensions

Aoxuan Wang, Fabrice Toussaint, Thomas Gibaud ยท 2026

Discontinuous shear thickening (DST) in dense suspensions leads to flow instabilities that limit processing in many systems. While high-power ultrasound has been reported to reduce the apparent viscosโ€ฆ

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Optimisation of a silicon-tungsten electromagnetic calorimeter energy response to photons

Yukun Shi, Vincent Boudry ยท 2026

An innovative path for the detectors at future colliders to achieve higher performances is to use a Particle Flow approach, which requires highly granular calorimeters to image individual showers. Theโ€ฆ

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Heisenberg-limited Hamiltonian learning without short-time control

Myeongjin Shin, Junseo Lee, Changhun Oh ยท 2026

Characterizing quantum systems by learning their underlying Hamiltonians is a central task in quantum information science. While recent algorithmic advances have achieved near-optimal efficiency in thโ€ฆ

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Couch-Torrence conformal inversion, supersymmetry and conserved charges for D3-branes

Mohammad Akhond, Massimo Bianchi, Antonio Cristofaro, Fabio Riccioni ยท 2026

An asymptotically flat spacetime in $D=4$ can be mapped via Couch-Torrence conformal inversion to the geometry around an extremal non-expanding and non-rotating horizon. At the linearized level, an inโ€ฆ

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YOSO: single-frame Gerchberg-Saxton phase retrieval with AI-based data augmentation for in-line holography

Julianna Winnik, Adam Walocha, Wojciech Ogonowski, Wiktor Forjasz, Piotr Arcab, Miko{l}aj Rogalski, Aleksandra Rutkowska, Marzena Stefaniuk, Jose Angel Picazo-Bueno, Vicente Mico, Maciej Trusiak, Maria Cywinska ยท 2026

We present YOSO (You Only Shot Once), a single-frame phase retrieval framework for digital in-line holographic microscopy (DIHM) in which supervised deep learning is used to numerically generate an adโ€ฆ

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Data-Efficient Indentation Size Effect Correction in Steels Using Machine Learning and Physics-Guided Augmentation

Radmir Karamov, Tagir Karamov ยท 2026

Shallow nanoindentation enables mechanical characterization of thin films, individual phases and other volume-constrained materials, but measured hardness is often inflated by the indentation size effโ€ฆ

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Cahn-Hilliard Phase Field modelling captures nanoscale contact line dynamics on high-friction surfaces

Michele Pellegrino, Parvathy K. Kannan, Gustav Amberg, Shervin Bagheri, Outi Tammisola, Berk Hess ยท 2026

Incorporating molecular-scale effects in the description of contact line motion is essential for accurately capturing all sources of energy dissipation in wetting dynamics. This holds particularly truโ€ฆ

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VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials

Rogerio Almeida Gouvea, Gian-Marco Rignanese ยท 2026

While machine-learned interatomic potentials (MLIPs) accelerate phonon dispersion calculations, merely identifying dynamical instabilities in computationally predicted materials is insufficient; automโ€ฆ

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Radio signal generation in milliseconds: enabling multi-parameter reconstruction of ultra-high-energy cosmic rays

Arsene Ferriere (for the GRAND Collaboration) ยท 2026

In recent years, radio detection of ultra-high-energy cosmic rays (UHECRs), with energies above $10^{18}$ eV, has become an established technique. The radio emissions can be simulated with high accuraโ€ฆ

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Effective Noise Mitigation via Quantum Circuit Learning in Quantum Simulation of Integrable Spin Chains

Wenlong Zhao, Yimeng Zhang, Yan Guo, Yufan Cui, Zhuohang Wang, Rui-Dong Zhu ยท 2026

We propose a noise-mitigation quantum simulation strategy for near-term quantum devices based on Quantum Circuit Learning (QCL), which is in particular effective for integrable quantum spin chains. Thโ€ฆ

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