Results 81 to 90 of about 2,854,398 (286)
3D‐Printed Gradient Shape‐Memory Strain Sensors With Rewritable Gauge Factors
A 3D‐printed shape‐memory strain sensor combines thermomechanical programming with gradient architectural design. Preset strain rewrites the gauge factor (GF), while the gradient geometry amplifies the response through strain redistribution. Reprogrammable sensitivity enables the same sensor to detect weak pulse signals and monitor large‐amplitude ...
Xuan Zhang +10 more
wiley +1 more source
RSRM: Reinforcement Symbolic Regression Machine
In nature, the behaviors of many complex systems can be described by parsimonious math equations. Automatically distilling these equations from limited data is cast as a symbolic regression process which hitherto remains a grand challenge.
Xu, Yilong, Liu, Yang, Sun, Hao
core
Hierarchical linear regression analyses predicting performance in non-symbolic and symbolic ordering in Grade 1 by processing speed, working memory storage and manipulation, as well as non-symbolic and symbolic numerical skills.
Karin Landerl (3713956) +6 more
core +1 more source
Diffusion-Based Symbolic Regression
Diffusion has emerged as a powerful framework for generative modeling, achieving remarkable success in applications such as image and audio synthesis. Enlightened by this progress, we propose a novel diffusion-based approach for symbolic regression. We construct a random mask-based diffusion and denoising process to generate diverse and high-quality ...
Zachary Bastiani +3 more
openaire +3 more sources
Role of Structural Disorder on Phonon Transport and Thermal Conductivity in Li6PS5Br
Solid electrolytes are fast ion and slow heat conductors. Although both transport properties are governed by lattice dynamics and atomic structure, their interplay remains poorly understood. Here, we study this interrelation in anion‐ordered and disordered Li6PS5Br.
Lukas Ketter +4 more
wiley +1 more source
Variable sensitivity values of the parameters studied through symbolic regression.
Variable sensitivity values of the parameters studied through symbolic regression.
Meenu R. Mridula (4891972) +2 more
core +1 more source
Linear regression analyses predicting change in non-symbolic and symbolic ordering from Grade 1–2 by processing speed, working memory storage and manipulation, non-symbolic and symbolic comparison, as well as counting.
Karin Landerl (3713956) +6 more
core +1 more source
Population Dynamics in Genetic Programming for Dynamic Symbolic Regression
This paper investigates the application of genetic programming (GP) for dynamic symbolic regression (SR), addressing the challenge of adapting machine learning models to evolving data in practical applications.
Philipp Fleck +2 more
doaj +1 more source
Configuration‐Encoded Cell Alignment by Light‐Gated Silicon Photovoltaic Microcell Arrays
Light‐gated silicon photovoltaic microcells convert pulsed illumination into localized electrical cues. Arranging otherwise similar microcells into four‐lobed, six‐lobed, or circular patterns directs fibroblasts into corresponding local arrangements without wired stimulation.
Yifan Tao +7 more
wiley +1 more source
Symbolic Regression for the Determination of Joint Roughness Coefficient
In this study, a novel symbolic regression-based empirical equation has been developed to compute the joint roughness coefficient (JRC) value based on the statistical parameters of rock joints.
Yuyang Zhao, Hongbo Zhao
doaj +1 more source

