Expertini Research Research

Browse Research Papers

28,154+ open-access research outputs.

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

Inverse-Designed Phase Prediction in Digital Lasers Using Deep Learning and Transfer Learning

Yu-Che Wu, Kuo-Chih Chang, Shu-Chun Chu ยท 2025

Digital lasers control the laser beam by dynamically updating the phase patterns of the spatial light modulator (SLM) within the laser cavity. Due to the presence of nonlinear effects, such as mode coโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Data-Driven Calibration of Large Liquid Detectors with Unsupervised Learning

Scott DeGraw, Steve Biller, Armin Reichold ยท 2025

This paper demonstrates a novel method to extract photomultiplier tube (PMT) calibration timing constants in large liquid scintillation detectors from physics data using the machinery of unsupervised โ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Active learning emulators for nuclear two-body scattering in momentum space

A. Giri, J. Kim, C. Drischler, Ch. Elster, R. J. Furnstahl ยท 2025

We extend the active learning emulators for two-body scattering in coordinate space with error estimation, recently developed by Maldonado et al. [Phys. Rev. C 112, 024002], to coupled-channel scatterโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Quantum Wasserstein distance for Gaussian states

Anaelle Hertz, Mohammad Ahmadpoor, Oleksandr Dzhenzherov, Augusto Gerolin, Khabat Heshami ยท 2025

Optimal transport between classical probability distributions has been proven useful in areas such as machine learning and random combinatorial optimization. Quantum optimal transport, and the quantumโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Domain-Aware Quantum Circuit for QML

Gurinder Singh, Thaddeus Pellegrini, Kenneth M. Merz, Jr ยท 2025

Designing parameterized quantum circuits (PQCs) that are expressive, trainable, and robust to hardware noise is a central challenge for quantum machine learning (QML) on noisy intermediate-scale quantโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Implicit Likelihood Inference of the Neutrino Mass Hierarchy from Cosmological Data

Ke Wang ยท 2025

In this paper, we turn to the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline to perform a multi-round ILI of the neutrino mass hierarchy from cosmological data, including $TT$,โ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

HydroGym: A Reinforcement Learning Platform for Fluid Dynamics

Christian Lagemann, Sajeda Mokbel, Miro Gondrum, Mario Ruttgers, Jared Callaham, Ludger Paehler, Samuel Ahnert, Nicholas Zolman, Kai Lagemann, Nikolaus Adams, Matthias Meinke, Wolfgang Schroder, Jean-Christophe Loiseau, Esther Lagemann, Steven L. Brunton ยท 2025

Modeling and controlling fluid flows is critical for several fields of science and engineering, including transportation, energy, and medicine. Effective flow control can lead to, e.g., lift increase,โ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

CoBiTS: Single-detector discrimination of binary black hole signals from glitches using deep learning

Matthew VanDyke, Kexuan Wu, Sukanta Bose ยท 2025

We develop a Conformer neural network, called Conformer Binary neTwork Search, or CoBiTS, for distinguishing binary black hole (BBH) gravitational wave (GW) signals from non-Gaussian and non-stationarโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Deep Learning Enabled Nanoscale X-ray Photoemission Electron Microscopy (nanoXPEEM)

Aashwin Mishra, Daniel Ratner, Quynh Nguyen ยท 2025

Understanding and manipulating two-dimensional materials for real-world applications remains challenging due to a lack of effective and high-throughput characterization techniques. Soft X-ray time-of-โ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Enhancing Reconstruction Capability of Wavelet Transform Amorphous Radial Distribution Function via Machine Learning Assisted Parameter Tuning

Deriyan Senjaya, Stephen Ekaputra Limantoro ยท 2025

Understanding atomic structures is crucial, yet amorphous materials remain challenging due to their irregular and non-periodic nature. The Wavelet Transform Radial Distribution Function (WT-RDF) offerโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

A Search for Binary Black Hole Mergers in LIGO O1-O3 Data with Convolutional Neural Networks

Ethan Silver, Plamen Krastev, Edo Berger ยท 2025

Since the first detection of gravitational waves in 2015 by LIGO from the binary black hole merger GW150914, gravitational-wave astronomy has developed significantly, with over 200 compact binary mergโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

A robust morphological classification method for galaxies using dual-encoding contrastive learning and multi-clustering voting on JWST/NIRCam images

Xiaolei Yin, Guanwen Fang, Shiying Lu, Zesen Lin, Yao Dai, Chichun Zhou ยท 2025

The two-step galaxy morphology classification framework {\tt USmorph} successfully combines unsupervised machine learning (UML) with supervised machine learning (SML) methods. To enhance the UML step,โ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Another Fit Bites the Dust: Conformal Prediction as a Calibration Standard for Machine Learning in High-Energy Physics

Jack Y. Araz, Michael Spannowsky ยท 2025

Machine-learning techniques are essential in modern collider research, yet their probabilistic outputs often lack calibrated uncertainty estimates and finite-sample guarantees, limiting their direct uโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

How gluon leading singularities discover curves on surfaces

Sergio Carrolo, Carolina Figueiredo ยท 2025

We study the leading singularities for pure gluon amplitudes obtained by on-shell gluing of three-particle amplitudes for an arbitrary graph in any number of dimensions. By encoding the polarization vโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Economical Jet Taggers -- Equivariant, Slim, and Quantized

Antoine Petitjean, Tilman Plehn, Jonas Spinner, Ullrich Kothe ยท 2025

Modern machine learning is transforming jet tagging at the LHC, but the leading transformer architectures are large, not particularly fast, and training-intensive. We present a slim version of the L-Gโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Discovering gravitational waveform distortions from lensing: a deep dive into GW231123

Juno C. L. Chan, Jose Maria Ezquiaga, Rico K. L. Lo, Joey Bowman, Lorena Magana Zertuche, Luka Vujeva ยท 2025

Gravitational waves (GWs) are unique messengers as they travel through the Universe without alteration except for gravitational lensing. Their long wavelengths make them susceptible to diffraction by โ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Machine learning assisted high throughput prediction of moir\'e materials

Daniel Kaplan, Alexander C. Tyner, Eva Y. Andrei, J. H. Pixley ยท 2025

The world of 2D materials is rapidly expanding with new discoveries of stackable and twistable layered systems composed of lattices of different symmetries, orbital character, and structural motifs. Oโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Efficient Monte Carlo sampling of metastable systems using non-local collective variable updates

Christoph Schonle, Davide Carbone, Marylou Gabrie, Tony Lelievre, Gabriel Stoltz ยท 2025

Monte Carlo simulations are widely used to simulate complex molecular systems, but standard approaches suffer from metastability. Lately, the use of non-local proposal updates in a collective-variableโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Next-to-Leading Order corrections to the Next-to-Eikonal DIS structure functions

Tolga Altinoluk, Guillaume Beuf, Jules Favrel, Michael Fucilla ยท 2025

We compute next-to-leading order (NLO) corrections to next-to-eikonal (NEik) quark background contributions to DIS structure functions. Among NEik corrections, $t$-channel quark exchanges provide the โ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Simulation-based inference with neural posterior estimation applied to X-ray spectral fitting -- III Deriving exact posteriors with dimension reduction and importance sampling

Didier Barret, Simon Dupourque ยท 2025

Simulation-based inference (SBI) with neural posterior estimation (NPE) provides rapid X-ray spectral fitting in both Gaussian and Poisson regimes by learning approximate parameter posteriors from simโ€ฆ

Read Paper โ†’
โ† Prev Page 88 of 1408 Next โ†’