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

Adaptive shape control for microswimmer navigation in turbulence

Jingran Qiu, Lorenzo Piro, Luca Biferale, Massimo Cencini, Bernhard Mehlig, Kristian Gustavsson ยท 2026

Navigation in turbulent environments is a fundamental challenge for biological and artificial microswimmers. While most existing studies focus on adapting motility or steering, the role of active morpโ€ฆ

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Physics Preprint PDF DOI

Role of photonic interference in exciton-mediated magneto-optic responses

Guven Budak, Christian Riedel, Akashdeep Kamra, Patrick Rinke, Christian Back, Matthias Stosiek, Florian Dirnberger ยท 2026

Coupled optical and magnetic excitations can give rise to remarkably strong magneto-optic responses. This is particularly evident in van der Waals magnets, such as the antiferromagnet CrSBr, where excโ€ฆ

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Bayesian neural network with autoencoder for model-based description of $\alpha$-particle preformation factor

Xiao-Yan Zhu, Heng-Jian Si-Tu, Hao Zhang, Wei Gao, Wen-Bin Lin, Xiao-Hua Li ยท 2026

$\alpha$ decay is an important probe for studying the structure of heavy and superheavy nuclei, in which the $\alpha$-particle preformation ($P_{\alpha}$) is a key physical quantity for describing decโ€ฆ

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Physics Preprint PDF DOI

AI Agents, Language, Deep Learning and the Next Revolution in Science

Ke Li, Beijiang Liu, Bruce Mellado, Changzheng Yuan, Zhengde Zhang ยท 2026

Modern science is reaching a critical inflection point. Instruments across disciplines, from particle physics and astronomy to genomics and climate modeling, now produce data of such scale, diversity,โ€ฆ

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Physics Preprint PDF DOI

Geometry and Mechanics of Multistable Origami Blocks

Munkyun Lee ยท 2026

Origami, which transforms flat sheets into three-dimensional shapes through folding patterns, has inspired the emergence of deployable systems in architecture and civil realms. Most existing origami-iโ€ฆ

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Physics Preprint PDF DOI

Machine Learning for Electrode Materials: Property Prediction via Composition

Hao Wu, Cameron Hargreaves, Arpit Mishra, Gian-Marco Rignanese ยท 2026

In this work, we benchmark three leading Machine Learning (ML) frameworks-MODNet, CrabNet, and a random forest model based on Magpie feature-for predicting properties of battery electrode materials usโ€ฆ

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Physics Preprint PDF DOI

Lindbladian Learning with Neural Differential Equations

Timothy Heightman, Roman Aseguinolaza Gallo, Edward Jiang, JRM Saavedra, Antonio Acin, Marcin P{l}odzien ยท 2026

Inferring the dynamical generator of a many-body quantum system from measurement data is essential for the verification, calibration, and control of quantum processors. When the system is open, this tโ€ฆ

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A fully open-source framework for streaming and cloud-processing of low-field MRI data

T. Guallart-Naval, J. Stairs, J. M. Algarin, H. Xue, J. Benlloch, P. Benlloch, J. Borreguero, J. Conejero, F. Galve, P. Garcia-Cristobal, M. Lacalle, B. Lena, L. Porcar, S. J. Schiff, A. Webb, M. Hansen, J. Alonso ยท 2026

Purpose: To present a fully open-source framework for quasi-real-time streaming and cloud-based processing of low-field (LF) MRI data, addressing the growing computational demands of advanced reconstrโ€ฆ

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Meta-PINNs: Meta-Learning Enhanced Physics-Informed Machine Learning Framework for Turbomachinery Flow Predictions under Varying Operation Conditions

Yuling Han, Zhihui Li, Zhibin Yu ยท 2026

Coupling physics with machine learning models has shown great potential for solving fluid dynamics problems governed by partial differential equations. However, conventional methods, such as physics-iโ€ฆ

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DeepConf: Machine Learning Conformer Reconstruction of Biomolecules from Scanning Tunneling Microscopy Images

Tim J. Seifert, Dhaneesh Kumar, Markus Etzkorn, Stephan Rauschenbach, Klaus Kern, Kelvin Anggara, Uta Schlickum ยท 2026

Improving the detailed understanding of the underlying properties and functions of biomolecules has recently attracted growing interest, enabled by the possibility of real-space imaging of single, intโ€ฆ

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Machine Learning Based Identification of Solvents from Post-Desiccation Patterns

Jesus Israel Moran-Cortes, Felipe Pacheco-Vazquez ยท 2026

We introduce an optimized protocol of fracture pattern classification using an artificial neural network to identify the solvent involved in the desiccation cracking process of starch-liquid slurries,โ€ฆ

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Quantum Minimal Learning Machine: A Fidelity-Based Approach to Error Mitigation

Clemens Lindner, Joonas Hamalainen, Matti Raasakka ยท 2026

We introduce the concept of quantum minimal learning machine (QMLM), a supervised similarity-based learning algorithm. The algorithm is conceptually based on a classical machine learning model and adoโ€ฆ

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A Perspective on Training Machine Learning Force Fields for Solid-State Electrolyte Materials

Zihan Yan, Shengjie Tang, Yizhou Zhu ยท 2026

Machine learning force fields enable high-accuracy modeling of solid-state electrolytes (SSEs). This perspective evaluates dataset size, reference quality, and model architectures. We show that rigid โ€ฆ

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AI based design of 2D material integrated optical polarizers

Rong Wang, Di Jin, Junkai Hu, Wenbo Liu, Yuning Zhang, Irfan H. Abidi, Sumeet Walia, Baohua Jia, Duan Huang, Jiayang Wu, David J. Moss ยท 2026

On-chip integration of highly anisotropic two-dimensional (2D) materials offers new opportunities for realizing high performance polarization selective devices. Obtaining optimized designs for such deโ€ฆ

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Machine Learning Techniques for Enhancing Quantum Key Distribution

Ali Al-Kuwari, Safaa Alqrinawi, Lujayn Al-Amir, Amina Mollazehi, Saif Al-Kuwari ยท 2026

Quantum Key Distribution (QKD) offers theoretically unbreakable security by leveraging quantum mechanics. However, practical implementation is challenged by environmental vulnerabilities, noise, and hโ€ฆ

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To What Extent Are Star Cluster Ages Encoded in Their Environments? Exploring the Spatial Distribution of Age-Related Information with PHANGS-HST Imaging and Convolutional Neural Networks

Javier Viana, Janice C. Lee, Andrew Vanderburg, John F. Wu, M. Jimena Rodriguez, Remy Indebetouw, Mederic Boquien, Ralf S. Klessen, Sophia Rivera, Erik Rosolowsky, Oleg Y. Gnedin, Daniel A. Dale, Kirsten L. Larson, David A. Thilker, Gagandeep Anand ยท 2026

The environments around star clusters evolve as stellar feedback reshapes the interstellar medium and dynamical processes reorganize the structure of the surrounding stellar field. As approximately siโ€ฆ

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Spin Neural Network Potential for Magnetic Phase Transitions in Uranium Dioxide

Keita Kobayashi, Hiroki Nakamura, Mitsuhiro Itakura ยท 2026

Uranium dioxide (UO2) is a prototypical nuclear fuel material, yet predicting its thermophysical properties across a wide temperature range remains challenging. One factor contributing to this difficuโ€ฆ

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Scalable optical neural network with nonlocally coupled coherent photonic processor

Chun Ren, Ryota Tanomura, Kazuki Ichinose, Keigo Mizukami, Yoshitaka Taguchi, Taichiro Fukui, Yoshiaki Nakano, Takuo Tanemura ยท 2026

Optical neural networks (ONNs) based on programmable photonic integrated circuits (PICs) offer a promising route toward low-latency and energy-efficient deep learning. However, conventional photonic iโ€ฆ

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Efficiently Learning Global Quantum Channels with Local Tomography

Zidu Liu, Dominik S. Wild ยท 2026

Scalable characterization of quantum processors is crucial for mitigating noise and imperfections. While randomized measurement protocols enable efficient access to local observables, inferring a globโ€ฆ

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Combined Garvey Kelson Relations for Mass Determinations and Machine Learning

I. Bentley, A. Fiorito III, M. Gebran, W. S. Porter, A. Aprahamian ยท 2026

Simple Garvey Kelson mass relations applied in two regions are often used as an evaluation metric for machine learning based mass models. These relations have also been used in the training of some maโ€ฆ

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