Results 51 to 60 of about 2,291 (214)

Smart Flexible Tactile Sensors: Recent Progress in Device Designs, Intelligent Algorithms, and Multidisciplinary Applications

Advanced Intelligent Discovery, EarlyView.
Flexible tactile sensors have considerable potential for broad application in healthcare monitoring, human–machine interfaces, and bioinspired robotics. This review explores recent progress in device design, performance optimization, and intelligent applications. It highlights how AI algorithms enhance environmental adaptability and perception accuracy
Siyuan Wang, Ming Dong, Rongrong Bao, Caofeng Pan   +3 more
wiley   +1 more source

Deflection of foundation pit retaining wall in heterogeneous soil strata: Intelligent prediction methods based on physics-informed machine learning

Journal of Rock Mechanics and Geotechnical Engineering
Excavation-induced retaining wall deflection (RWD) significantly influences the safety of surrounding built environment. To predict the three-dimensional RWD in heterogeneous strata, a new partial differential equation (PDE) is derived in this study, and
Cong Zhou, Lei He, Junchen He, Yi Zhang, Huaiguang Xiao, Chee Kiong Soh   +5 more
doaj   +1 more source

A Multimodal Intelligent System for Human Digital Twin Simulation with Continuous Kinematic Data Tracking, Biometric Prognosis, and Cognitive State Feedback in Industrial Environments

Advanced Intelligent Discovery, EarlyView.
This article implements a unified human digital twin framework that integrates cutting edge actuation, sensing, simulation, and bidirectional feedback capability. The approach includes integrating multimodal sensing, AI, and biomechanical simulation into one compact system.
Tajbeed Ahmed Chowdhury, Sebastian Gratz‐Kelly, Eric Wagner, Paul Motzki, Martina Lehser   +4 more
wiley   +1 more source

A PINN framework for inverse physical design of metal-loaded electromagnetic devices

AIP Advances
To solve the difficulty of inverse design of metal-loaded electromagnetic devices, we propose a physics-informed neural network (PINN) framework. With the emergence of PINNs, some scholars within the field of electromagnetism have utilized them to design
Yu-Hang Liu, Jin-Pin Liu, Bing-Zhong Wang, Ren Wang   +3 more
doaj   +1 more source

On the Hard Boundary Constraint Method for Fluid Flow Prediction based on the Physics-Informed Neural Network

Applied Sciences
With the rapid development of artificial intelligence technology, the physics-informed neural network (PINN) has gradually emerged as an effective and potential method for solving N-S equations.
Zixu Xiao, Yaping Ju, Zhen Li, Jiawang Zhang, Chuhua Zhang   +4 more
doaj   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Advanced Intelligent Discovery, EarlyView.
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +1 more source

Physics–Informed Neural Network With Enhanced Parameter–State Coupling for Inverse Modeling of Unsaturated Flow in Heterogeneous Soils

Water Resources Research
Modeling unsaturated flow remains challenging due to the interplay of uncertain atmospheric forcing, parameter heterogeneity, and sparse observations.
Xuezi Gong, Yuanyuan Zha
doaj   +1 more source

Disentangling Aleatoric and Epistemic Uncertainty in Physics‐Informed Neural Networks: Application to Insulation Material Degradation Prognostics

Advanced Intelligent Systems, EarlyView.
Physics‐Informed Neural Networks (PINNs) provide a framework for integrating physical laws with data. However, their application to Prognostics and Health Management (PHM) remains constrained by the limited uncertainty quantification (UQ) capabilities.
Ibai Ramirez, Jokin Alcibar, Joel Pino, Mikel Sanz, Jose I. Aizpurua   +4 more
wiley   +1 more source

An Adaptive Weight Physics-Informed Neural Network for Vortex-Induced Vibration Problems

Buildings
Vortex-induced vibration (VIV) is a common fluid–structure interaction phenomenon in practical engineering with significant research value. Traditional methods to solve VIV issues include experimental studies and numerical simulations.
Ping Zhu, Zhonglin Liu, Ziqing Xu, Junxue Lv   +3 more
doaj   +1 more source