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

Foundation-Model Surrogates Enable Data-Efficient Active Learning for Materials Discovery

Jeffrey Hu, Rongzhi Dong, Ying Feng, Ming Hu, Jianjun Hu ยท 2026

Active learning (AL) has emerged as a powerful paradigm for accelerating materials discovery by iteratively steering experiments toward promising candidates, reducing the number of costly synthesis-anโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Physics-Guided Inverse Design of Optical Waveforms for Nonlinear Electromagnetic Dynamics

Hao Zhang, Jack Hirschman, Randy Lemons, Nicole R. Neveu, Joseph Robinson, Auralee L. Edelen, Tor O. Raubenheimer, Dan Wang, Ji Qiang, Sergio Carbajo ยท 2026

Structured optical waveforms are emerging as powerful control fields for the next generation of complex photonic and electromagnetic systems, where the temporal structure of light can determine the ulโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

CLARE: Classification-based Regression for Electron Temperature Prediction

Michael Liang, Blake DeHaas, Naomi Maruyama, Xiangning Chu, Takumi Abe, Koh-Ichiro Oyama ยท 2026

Electron temperature (Te) is an important parameter governing space weather in the upper atmosphere, but has historically been underexplored in the space weather machine learning literature. We presenโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Calabi-Yau Metrics with K\"ahler Moduli Dependence

Andrei Constantin, Andre Lukas, Luca A. Nutricati ยท 2026

We present a method to construct approximate analytic expressions for Ricci-flat K\"ahler metrics on Calabi-Yau threefolds with explicit dependence on the K\"ahler moduli. Our strategy combines numeriโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Optimal Experimental Design for Reliable Learning of History-Dependent Constitutive Laws

Kaushik Bhattacharya, Lianghao Cao, Andrew Stuart ยท 2026

History-dependent constitutive models serve as macroscopic closures for the aggregated effects of micromechanics. Their parameters are typically learned from experimental data. With a limited experimeโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Robustness of Neural Networks for CMB Polarization Foreground Removal

Luca Gomez Bachar, Cora Dvorkin, Alberto Daniel Supanitsky ยท 2026

The detection of Cosmic Microwave Background primordial $B$-mode polarization would constitute a ``smoking gun" signal of primordial gravitational waves. However, this measurement requires accurate reโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Pruning-induced phases in fully-connected neural networks: the eumentia, the dementia, and the amentia

Haining Pan, Nakul Aggarwal, J. H. Pixley ยท 2026

Modern neural networks are heavily overparameterized, and pruning, which removes redundant neurons or connections, has emerged as a key approach to compressing them without sacrificing performance. Hoโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Metadensity functional learning for classical fluids: Regularizing with pair correlations

Stefanie M. Kampa, Florian Sammuller, Matthias Schmidt ยท 2026

We investigate and exploit consequences of the recent neural metadensity functional theory [Kampa et al., Phys. Rev. Lett. 134, 107301 (2025), 10.1103/PhysRevLett.134.107301] for describing the physicโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

From vacuum amplitudes to qubits

German Rodrigo ยท 2026

High-energy colliders, exemplified by the CERN's Large Hadron Collider (LHC), constitute genuine quantum machines. In alignment with Richard Feynman's foundational vision for quantum computing, collidโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Identifying highly magnetized white dwarfs: A dimensionality reduction framework for estimating magnetic fields

Surajit Kalita (Warsaw), Akhil Uniyal (TDLI), Tomasz Bulik (Warsaw), Yosuke Mizuno (TDLI) ยท 2026

Magnetic fields play a crucial role in compact object physics, particularly in white dwarfs (WDs), where high densities can sustain strong magnetic fields. Observations have revealed magnetized WDs (Mโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Gaussian Process Regression-based Knowledge Distillation Framework for Simultaneous Prediction of Physical and Mechanical Properties of Epoxy Polymers

Sindu B.S., Jan Hamaekers ยท 2026

Epoxy polymers are widely used due to their multifunctional properties, but machine learning (ML) applications remain limited owing to their complex 3D molecular structure, multi-component nature, andโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Melting of thin silicon films: a molecular dynamics study with two machine learning potentials

Yu. D. Fomin, E. N. Tsiok, V. N. Ryzhov ยท 2026

Thermal stability of silicene and thin silicon films is studied by molecular dynamics using two machine-learning potentials, SNAP and GAP. For SNAP potential, systems ranging from a single silicene laโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

pyKurucz: A Pure Python Reimplementation of Kurucz SYNTHE for Stellar Spectrum Synthesis

Elliot M. Kim, Yuan-Sen Ting ยท 2026

pyKurucz is a pure Python reimplementation of Kurucz's SYNTHE, the standard code for computing synthetic stellar spectra. The original Fortran, written decades ago in a legacy dialect, is difficult toโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Machine Learning of Topological Insulator and Anderson Insulator in One-Dimensional Extended Su-Schrieffer-Heeger Chain

Zhekai Yin, C. K. Ong ยท 2026

We study disorder effects in the extended Su-Schrieffer-Heeger (SSH) model using a convolutional neural network (CNN) trained on reduced correlation matrices (RCMs) of disorder-free systems to predictโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Exploring the Viability of Fisher Discriminants in Galaxy Morphology Classification

Sazatul Nadhilah Zakaria, Santtosh Muniyandy, John Y. H. Soo ยท 2026

One of the major challenges in astronomy involves accurately classifying galaxies, particularly distinguishing between different galaxy types. While many complex algorithms have shown strong performanโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

High-pressure phase stability and superconductivity in La-Zr-H hydrides

Ijaz shahid, Maxim A. Grebeniuk, Jinbin Zhao, Ergen Bao, Tianye Yu, Xiangyang Liu, Yi-Chi Zhang, Artem R. Oganov, Yan Sun, Peitao Liu, Xing-Qiu Chen ยท 2026

Hydrogen-rich ternary hydrides are promising candidates for high-Tc superconductivity at megabar pressures, yet their chemical space is vast and largely unexplored. Combining evolutionary structure seโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Tearing Stability Prediction Combining Toroidal Calculations With a Two-Fluid Slab Layer

D.A. Burgess, N.C. Logan, J.-K. Park, C. Paz-Soldan ยท 2026

A new classical TM stability simulation workflow has been developed that solves the resistive inner-layer equations in a plasma slab to yield a linear, quasi-toroidal TM growth rate $\gamma$ and mode โ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Quantum mechanical framework for quantization-based optimization: from Gradient flow to Schroedinger equation

Jinwuk Seok, Changsik Cho ยท 2026

This work presents a quantum mechanical framework for analyzing quantization-based optimization algorithms. The sampling process of the quantization-based search is modeled as a gradient-flow dissipatโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Meta-generalized gradient approximation made in the Hartree gauge

Yan Oueis, Akilan Ramasamy, James W. Furness, Jamin Kidd, Timo Lebeda, Jianwei Sun ยท 2026

In density functional theory (DFT), exact constraints, fundamental mathematical properties of the exchange-correlation (XC) energy and its underlying XC hole, along with paradigm systems such as the uโ€ฆ

Read Paper โ†’
Physics Preprint PDF DOI

Atomic-Scale Mechanisms of SiO$_2$ Plasma-Enhanced Chemical Vapor Deposition Revealed by Molecular Dynamics with a Machine-Learning Interatomic Potential

Jaehoon Kim, Minseok Moon, Hyunsung Cho, Hyeon-Deuk Kim, Rokyeon Kim, Gyehyun Park, Seungwu Han, Youngho Kang ยท 2026

Plasma-enhanced chemical vapor deposition (PECVD) of silicon dioxide (SiO$_2$) is widely used for low-temperature fabrication of dielectric thin films, yet its atomic-scale growth mechanisms remain inโ€ฆ

Read Paper โ†’
โ† Prev Page 36 of 1408 Next โ†’