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

Neural Operator Representation of Granular Micromechanics-based Failure Envelope

Jinkyo Han, Payam Poorsolhjouy, Bahador Bahmani ยท 2026

Micromechanics-based granular models are widely used to predict the failure behavior of porous and particulate materials, including concrete, soils, foams, and biological tissues. Although these modelโ€ฆ

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Physics Preprint PDF DOI

Predicting Redshift in Seyfert Galaxies Using Machine Learning

Uzay Aydin ยท 2026

Photometric redshift estimation is a key requirement for modern large-area surveys, where spectroscopic measurements are observationally prohibitive. Seyfert II galaxies provide a particularly challenโ€ฆ

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Physics Preprint PDF DOI

Option Pricing on Noisy Intermediate-Scale Quantum Computers: A Quantum Neural Network Approach

Sebastian Zajac, Rafa{l} Pracht ยท 2026

In a global derivatives market with notional values in the hundreds of trillions of dollars, the accuracy and efficiency of pricing models are of fundamental importance, with direct implications for rโ€ฆ

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Physics Preprint PDF DOI

Testing $\Lambda$CDM versus dynamical dark energy in one year: A DESI spectroscopic follow-up program for Rubin supernovae

Jannik Truong, Greg Aldering, Saul Perlmutter, David Rubin, David Schlegel ยท 2026

Combined cosmological probes currently indicate that best-fit values in the $w_0-w_a$ parametrization of dynamical dark energy deviate from $\Lambda$CDM by $\sim3\sigma$. In this work, we present a suโ€ฆ

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Benchmarking Quantum Kernel Support Vector Machines Against Classical Baselines on Tabular Data: A Rigorous Empirical Study with Hardware Validation

Siavash Kakavand, Christoph Strohmeyer, Michael Schlotter ยท 2026

Quantum kernel methods have been proposed as a promising approach for leveraging near-term quantum computers for supervised learning, yet rigorous benchmarks against strong classical baselines remain โ€ฆ

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Physics Preprint PDF DOI

Discovery of the First Octupole Pulsation Mode in a delta Scuti Star: A Stationary l = 3 Sectoral Mode

S. A. Rappaport, R. Jayaraman, G. Handler, D. Kurtz, V. Zhang, R. Gagliano, B. Powell, J. Fuller, T. Borkovits, V. Kostov, J. Daszynska-Daszkiewicz ยท 2026

Aims. We are attempting to better understand how stellar pulsations in close binary systems are affected, and possibly induced, by tidal, Coriolis, and centrifugal forces. Methods. We analyzed TESS โ€ฆ

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Physics Preprint PDF DOI

Machine Learning Supports Existence of Previously Unrecognized Transient Astronomical Phenomena in Historical Observatory Images

Stephen Bruehl, Brian Doherty, Alina Streblyanska, Beatriz Villarroel ยท 2026

Transient, star-like point sources that appear and vanish over short timescales are described in astronomical images prior to launch of Sputnik. We have reported that transient numbers diminish signifโ€ฆ

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Physics Preprint PDF DOI

Learning error suppression strategies for dynamic quantum circuits

Christopher Tong, Liran Shirizly, Edward H. Chen, Derek S. Wang, Bibek Pokharel ยท 2026

Dynamic quantum circuits integrate unitary evolution with mid-circuit measurement and feedforward, enabling conditional operations essential for efficient quantum algorithms and foundational for faultโ€ฆ

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Exponentially-improved effective descriptions of physical bosonic systems

Varun Upreti, Nicolas Quesada, Ulysse Chabaud ยท 2026

The effective description of a bosonic quantum system identifies the minimum finite dimension required to capture its essential dynamics. This effective dimension plays an important role in the compleโ€ฆ

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Harmoniq: Efficient Data Augmentation on a Quantum Computer Inspired by Harmonic Analysis

Kristina Kirova, Monika Doerfler, Franz Luef, Richard Kueng ยท 2026

Quantum machine learning has attracted significant interest in recent years. Most existing approaches, however, are variational in nature and require extensive parameter optimization subroutines. Hereโ€ฆ

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BBP transition and the leading eigenvector of the spiked Wigner model with inhomogeneous noise

Leonardo S. Ferreira, Fernando L. Metz ยท 2026

The spiked Wigner ensemble is a prototypical model for high-dimensional inference. We study the spectral properties of an inhomogeneous rank-one spiked Wigner model in which the variance of each entryโ€ฆ

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Physics-Informed Neural Networks for Maximizing Quantum Fisher Information in Time-Dependent Many-Body Systems

Antonio Ferrer-Sanchez, Yolanda Vives-Gilabert, Yue Ban, Xi Chen, Jose D. Martin-Guerrero ยท 2026

Quantum Fisher Information (QFI) sets the ultimate precision limit for parameter estimation and is therefore a central quantity in quantum metrology. In time-dependent many-body systems, however, maxiโ€ฆ

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Uncertainty-aware phase fraction prediction and active-learning-guided out-of-domain discovery of refractory multi-principal element alloys

A. K. Shargh, C. D. Stiles, J. A. El-Awady ยท 2026

Refractory multi-principal element alloys (RMPEAs) represent a novel class of alloys characterized by an extensive compositional design space and the potential for exceptional mechanical performance uโ€ฆ

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Autoregressive prediction of 2D MHD dynamics inferred from deep learning modeling

David Kivarkis, Waleed Mouhali, Sadruddin Benkadda, Kai Schneider ยท 2026

We develop two deep learning surrogate autoregressive models for the prediction of the temporal evolution of two-dimensional ideal magnetohydrodynamic (MHD) Kelvin-Helmholtz instabilities across a ranโ€ฆ

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Leading UV divergences of quantum corrections to K\"ahler superpotential in general $\mathcal{N}=1$ chiral model

R.M. Iakhibbaev, A. I. Mukhaeva, D.M. Tolkachev ยท 2026

Using the Bogoliubov-Parasiuk theorem we derive differential equations for the sum of leading UV divergences of the K\"ahler potential in the general $\mathcal{N}=1$ supersymmetric chiral theory. The โ€ฆ

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Materials Informatics Across the Length Scales

Jamal Abdul Nasir, Hamide Kavak, Oguzhan Der, Ali Ercetin, Amila Akagic, Jesper Friis, Francesca L. Bleken, Andrea Lorenzoni, Francesco Mercuri, Scott M. Woodley, Keith T. Butler ยท 2026

Materials informatics is increasingly used to support modelling, analysis and design across the length scales of materials science, from atomistic simulations to microstructural characterisation and cโ€ฆ

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Information decomposition for disentangled and interpretable manifold learning of fluid flows via variational autoencoders

Zhiyuan Wang, Iacopo Tirelli, Stefano Discetti, Andrea Ianiro ยท 2026

We introduce an information-theoretic framework that uses variational autoencoders (VAEs) to extract compact, physically interpretable manifolds from high-dimensional flow-field data. To this end, theโ€ฆ

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Replica Theory of Spherical Boltzmann Machine Ensembles

Thomas Tulinski (LPENS), Jorge Fernandez-De-Cossio-Diaz (IPHT, LPENS), Simona Cocco (LPENS), Remi Monasson ยท 2026

Training in machine learning generally consists in finding one model, whose parameters minimize a data-dependent loss. Yet, empirical work shows that ensemble learning, an approach in which multiple mโ€ฆ

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Holography and Optimal Transport: Emergent Wasserstein Spacetime in Harmonic Oscillator, SYK and Krylov Complexity

Koji Hashimoto, Norihiro Tanahashi ยท 2026

Optimal transport and Wasserstein distance are prominent tools to quantify the space of probability distributions. From a novel viewpoint of manifold hypothesis in machine learning being a possible guโ€ฆ

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From Flat-Optics Concept to Qualified Hardware: Skills Map for the Meta-Optics and Diffractive Optics Workforce

Ingrid Torres, Alex Krasnok ยท 2026

Flat optics is now judged by more than a strong simulation or a single laboratory demonstration. To reach release, a device must survive a chain of handoffs: requirements, model selection, verificatioโ€ฆ

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