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

A joint voxel flow - phase field framework for ultra-long microstructure evolution prediction with physical regularization

Ao Zhou, Salma Zahran, Chi Chen, Zhengyang Zhang, Yanming Wang ยท 2026

Phase-field (PF) modeling is a powerful tool for simulating microstructure evolution. To overcome the high computational cost of PF in solving complex PDEs, machine learning methods such as PINNs, conโ€ฆ

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Physics Preprint PDF DOI

Lateral Graphene-Metallene Interfaces at the Nanoscale

Mohammad Bagheri, Pekka Koskinen ยท 2026

Metallenes are atomically thin, nonlayered two-dimensional materials. While they have appealing properties, their isotropic metallic bonding makes their stabilization difficult and presents considerabโ€ฆ

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Physics Preprint PDF DOI

Optimization of Deep Learning Models for Radio Galaxy Classification

Philipp Denzel, Manuel Weiss, Elena Gavagnin, Frank-Peter Schilling ยท 2026

Modern radio telescope surveys, capable of detecting billions of galaxies in wide-field surveys, have made manual morphological classification impracticable. This applies in particular when the Squareโ€ฆ

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Physics Preprint PDF DOI

Scalable Dielectric Tensor Predictions for Inorganic Materials using Equivariant Graph Neural Networks

Haowei Hua, Chen Liang, Ding Pan, Irwin King, Shengchao Liu, Koji Tsuda, Wanyu Lin ยท 2026

Accurate prediction of dielectric tensors is essential for accelerating the discovery of next-generation inorganic dielectric materials. Existing machine learning approaches, such as equivariant graphโ€ฆ

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Physics Preprint PDF DOI

The Role of Quantum in Hybrid Quantum-Classical Neural Networks: A Realistic Assessment

Dominik Freinberger, Philipp Moser ยท 2026

Quantum machine learning has emerged as a promising application domain for near-term quantum hardware, particularly through hybrid quantum-classical models that leverage both classical and quantum proโ€ฆ

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Physics Preprint PDF DOI

Explainable machine learning classification of \textit{Chandra} X-ray sources: SHAP analysis of multi-wavelength features

Shivam Kumaran, Samir Mandal, Sudip Bhattacharyya ยท 2026

Extensive astronomical surveys, like those conducted with the {\em Chandra} X-ray Observatory, detect hundreds of thousands of unidentified cosmic sources. Machine learning (ML) methods offer an efficโ€ฆ

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Physics Preprint PDF DOI

SpectraFormer: an Attention-Based Raman Unmixing Tool for Accessing the Graphene Buffer-Layer Signature on SiC

Dmitriy Poteryayev, Pietro Novelli, Annalisa Coriolano, Riccardo Dettori, Valentina Tozzini, Fabio Beltram, Massimiliano Pontil, Antonio Rossi, Stiven Forti, Camilla Coletti ยท 2026

Raman spectroscopy is a key tool for graphene characterization, yet its application to graphene grown on silicon carbide (SiC) is strongly limited by the intense and variable second-order Raman responโ€ฆ

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Physics Preprint PDF DOI

JAX-Shock: A Differentiable, GPU-Accelerated, Shock-Capturing Neural Solver for Compressible Flow Simulation

Bo Zhang ยท 2026

Understanding shock-solid interactions remains a central challenge in compressiblefluiddynamics. WepresentJAX-Shock: afully-differentiable,GPU-accelerated, high-order shock-capturing solver for efficiโ€ฆ

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Physics Preprint PDF DOI

The Impact of Robotic Telescopes on Time-Domain Astronomy

Yakubu Mu'allim, J. O.Vwavware, A. Ohwofosirai ยท 2026

The field of time-domain astronomy has experienced unprecedented growth due to the increasing deployment of robotic telescopes capable of autonomous, round-the-clock sky monitoring. These instruments โ€ฆ

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Physics Preprint PDF DOI

A universal vision transformer for fast calorimeter simulations

Luigi Favaro, Andrea Giammanco, Claudius Krause ยท 2026

The high-dimensional complex nature of detectors makes fast calorimeter simulations a prime application for modern generative machine learning. Vision transformers (ViTs) can emulate the Geant4 responโ€ฆ

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Improved Lower Bounds for Learning Quantum Channels in Diamond Distance

Aadil Oufkir, Filippo Girardi ยท 2026

We prove that learning an unknown quantum channel with input dimension $d_A$, output dimension $d_B$, and Choi rank $r$ to diamond distance $\varepsilon$ requires $ \Omega\!\left( \frac{d_A d_B r}{\vaโ€ฆ

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Physics Preprint PDF DOI

Characterizing the physical and chemical properties of the Class I protostellar system Oph-IRS 44. Binarity, infalling streamers, and accretion shocks

E. Artur de la Villarmois, V. V. Guzman, M. L. van Gelder, E. F. van Dishoeck, E. A. Bergin, D. Harsono, N. Sakai, J. K. J{o}rgensen ยท 2026

(Abridged) In the low-mass star formation process, theoretical models predict that material from the infalling envelope could be shocked as it encounters the outer regions of the disk. Nevertheless, oโ€ฆ

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Physics Preprint PDF DOI

Equivariant Neural Networks for Force-Field Models of Lattice Systems

Yunhao Fan, Gia-Wei Chern ยท 2026

Machine-learning (ML) force fields enable large-scale simulations with near-first-principles accuracy at substantially reduced computational cost. Recent work has extended ML force-field approaches toโ€ฆ

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Modeling the Effect of C/O Ratio on Complex Carbon Chemistry in Cold Molecular Clouds

Alex N. Byrne, Christopher N. Shingledecker, Edwin A. Bergin, Martin S. Holdren, Gabi Wenzel, Ci Xue, Troy Van Voorhis, Brett A. McGuire ยท 2026

Elemental abundances, which are often depleted with respect to the solar values, are important input parameters for kinetic models of interstellar chemistry. In particular, the amount of carbon relatiโ€ฆ

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Physics Preprint PDF DOI

Autonomous Sampling and SHAP Interpretation of Deposition-Rates in Bipolar HiPIMS

Alexander Wieczorek, Nathan Rodkey, Jan Sommerhauser, Jason Hattrick-Simpers, Sebastian Siol ยท 2026

High-power impulse magnetron sputtering (HiPIMS) offers considerable control over ion energy and flux, making it invaluable for tailoring the microstructure and properties of advanced functional coatiโ€ฆ

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HI-bearing dark galaxies predictions from constrained Local Group simulations: how many and where to find them

Guacimara Garcia-Bethencourt, Arianna Di Cintio, Sebastien Comeron, Elena Arjona-Galvez, Ana Contreras-Santos, Salvador Cardona-Barrero, Chris B. A. Brook, Andrea Negri, Noam I. Libeskind, Alexander Knebe ยท 2026

Dark galaxies are small, DM-dominated halos whose gas remains in hydrostatic and thermal equilibrium and has never formed stars. They are of particular interest because they represent a strong predictโ€ฆ

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Physics Preprint PDF DOI

Material exploration through active learning -- METAL

Joakim Brorsson, Henrik Klein Moberg, Joel Hildingsson, Jonatan Gastaldi, Tobias Mattisson, Anders Hellman ยท 2026

The discovery and design of new materials are paramount in the development of green technologies. High entropy oxides represent one such group that has only been tentatively explored, mainly due to thโ€ฆ

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Towards an optimal extraction of cosmological parameters from galaxy cluster surveys using convolutional neural networks

Inigo Saez-Casares, Matteo Calabrese, Davide Bianchi, Marina S. Cagliari, Marco Chiarenza, Jean-Marc Christille, Luigi Guzzo ยท 2026

The possibility to constrain cosmological parameters from galaxy surveys using field-level machine learning methods that bypass traditional summary statistics analyses, depends crucially on our abilitโ€ฆ

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Filtering interlopers with photometry and diagnostic features: A machine learning framework validated with CSST slitless spectroscopy

Hui Peng, Yu Yu, Yiyang Guo, Yizhou Gu, Run Wen, Yunkun Han, Jipeng Sui, Hu Zou, Xiaohu Yang, Pengjie Zhang, Xian Zhong Zheng, Hong Guo, Yipeng Jing, Cheng Li, Hu Zhan, Gongbo Zhao ยท 2026

The slitless spectroscopic method employed by missions such as Euclid and the Chinese Space Station Survey Telescope (CSST) faces a fundamental challenge: spectroscopic redshifts derived from their daโ€ฆ

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DeepBessel: deep learning-based full-field vibration profilometry using single-shot time-averaged interference microscopy

Maria Cywinska, Wiktor Forjasz, Emilia Wdowiak, Michal Jozwik, Adam Styk, Krzysztof Patorski, Maciej Trusiak ยท 2026

Full-field vibration profilometry is essential for dynamic characterizing microelectromechanical systems (MEMS/MOEMS). Time-averaged interferometry (TAI) encodes spatial information about MEMS or MOEMโ€ฆ

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