Results 131 to 140 of about 143,004 (295)

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Dynamic externalities and regional manufacturing development: An exploration of the Polish experience before and after 1989 [PDF]

The impact of localization and urbanization economies on regional manufacturing development in Poland 1976-96 is assessed in terms of employment and the regional convergence or divergence of the economy. We examine current research on the role of dynamic
Jon M. Steineke, Roger Bivand
core  

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

Advanced Intelligent Discovery, EarlyView.
A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas, Panagiotis Krokidas, Vasileios Vavourakis, Clara Essmann, George Giannakopoulos   +4 more
wiley   +1 more source

The Fast Decay Process in Recreational Demand Activities and the use of Alternative Count Data Models

Since the early 1990s, researchers have routinely used count data models (such as the Poisson and negative binomial) to estimate the demand for recreational activities.
Surry, Yves R., Sarker, Rakhal
core   +2 more sources

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

AI‐Guided Co‐Optimization of Advanced Field‐Effect Transistors: Bridging Material, Device, and Fabrication Design

Advanced Intelligent Discovery, EarlyView.
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath, Shauryaraj K. Jilkar, Aditya Udgaonkar, Yu (Joe) Lu, Ali Mesbah   +4 more
wiley   +1 more source

Exploring the relationship between preferred routes for walking exercise and green space layout in a dense urban area: a series of poisson regression models

Journal of Asian Architecture and Building Engineering
The literature considers urban green spaces as a key to promoting physical activity and health. Despite the increasingly recognized walking exercise promotion benefits of urban green spaces, the relationship between walking exercise preferences and green
Ninglong You
doaj   +1 more source

Regression Models for Count Data in R

The classical Poisson, geometric and negative binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in the R system for statistical computing.
Simon Jackman, Achim Zeileis, Christian Kleiber   +2 more
core  

Modelling Truncated and Clustered Count Data

, 2005
Count response data often exhibit departures from the assumptions of standard Poisson generalized linear models (McCullagh & Nelder 1989). In particular, cluster level correlation of the data and truncation at zero are two common characteristics of ...
Saei, Ayoub, Chambers, Ray
core  

A Novel Parameter Estimation Method for Pneumatic Soft Hand Control Applying Logarithmic Decrement for Pseudo‐Rigid Body Modeling

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
In this research, a paradigm of parameter estimation method for pneumatic soft hand control is proposed. The method includes the following: 1) sampling harmonic damping waves, 2) applying pseudo‐rigid body modeling and the logarithmic decrement method, and 3) deriving position and force control.
Haiyun Zhang, Kelvin HoLam Heung, Gabrielle J. Naquila, Ashwin Hingwe, Ashish D. Deshpande   +4 more
wiley   +1 more source