Results 141 to 150 of about 2,349 (245)

DADD-PINN: Dual Adaptive Domain Decomposition Physics-Informed Neural Networks

Mathematics
When solving partial differential equations (PDEs), traditional Physics-Informed Neural Networks (PINNs) often encounter difficulties in capturing critical physical features and addressing information bias between subdomains.
Yunkang Xiong, Hongyu Wei, Zhiying Ma, Zhihong Ding, Yaxin Peng   +4 more
doaj   +1 more source

MP-PINN: A Multi-phase Physics-Informed Neural Network for Epidemic Forecasting

Forecasting temporal processes such as virus spreading in epidemics often requires more than just observed time-series data, especially at the beginning of a wave when data is limited. Traditional methods employ mechanistic models like the SIR family, which make strong assumptions about the underlying spreading process, often represented as a small set
Thang Nguyen, Dung Nguyen 0001, Kha Pham, Truyen Tran 0001   +3 more
openaire   +2 more sources

Programmable Fabric‐Based Soft Pneumatic Actuators for Wearables: A Review

Small Structures, Volume 7, Issue 6, June 2026.
This review summarizes recent advances in fabric soft pneumatic actuators (FSPAs), emphasizing programmable design strategies, diverse actuation deformation modes, material selection, and emerging applications. We highlight how geometric constraints, material distribution, and specialized fabrication methods govern longitudinal, bending, torsional, and
Zichao Ling, Zichun Cao, Shanshan Dong, Jie Zhang, Yadie Yang   +4 more
wiley   +1 more source

Inverse solution of process parameters in gear grinding using hierarchical bayesian physics informed neural network (HBPINN)

Scientific Reports
Accurate inverse solution of process parameters by surface roughness is crucial for precision gear grinding processes. When inversely solving process parameters, model parameters are typically obtained by fitting experimental data.
Qi Zhang, Qiang Zhang, Yongsheng Zhao, Yanming Liu, Zhi Wang, Yali Ma   +5 more
doaj   +1 more source

EV-PINN: A Physics-Informed Neural Network for Predicting Electric Vehicle Dynamics

CoRR
This work has been submitted to the 2025 IEEE International Conference on Robotics and Automation (ICRA) for possible ...
Hansol Lim, Jee Won Lee, Jonathan Boyack, Jongseong Brad Choi 0001   +3 more
openaire   +2 more sources

Loss-attentional physics-informed neural networks [PDF]

Physics-informed neural networks (PINNs) have emerged as a significant endeavour in recent years to utilize artificial intelligence technology for solving various partial differential equations (PDEs).
Yang, H., Taccari, M.L., Song, Y., Wang, H., Chen, X.   +4 more
core   +1 more source

An Overview of Artificial Intelligence and Machine Learning Approaches for Building Energy Analysis, Characterization, Control, and Grid Support Services Provision

WIREs Energy and Environment, Volume 15, Issue 2, June 2026.
Artificial Intelligence and Machine Learning Approaches used in Building Energy Analysis, Control, and Provision of Grid Support Services. ABSTRACT Increasing penetrations of variable renewable energy sources like wind and solar photovoltaic (PV) systems are challenging power system stability worldwide.
Jack S. Bryant, Hangxin Li, Nawanjana Maheepala, Xiaoyu Lin, Mingkun Dai, Binglong Han, Robin Guan, Lasantha Meegahapola, Shengwei Wang, Dilan Robert, Liuping Wang, Fu Xiao   +11 more
wiley   +1 more source

A physic-guided YOLO framework for pavement deformation distress detection

Scientific Reports
This research develops an automated methodology for detecting and assessing deformation pavement distress such as rutting and corrugation, using deep learning algorithms. Utilizing a dataset of road images, various YOLO algorithms were initially explored
Dorna Sheikholeslami, Amir Golroo, Ali Khodaii, Arya Daneshvar, Fereidoon Moghadas Nejad   +4 more
doaj   +1 more source

TE-PINN: Quaternion-Based Orientation Estimation using Transformer-Enhanced Physics-Informed Neural Networks

This paper introduces a Transformer-Enhanced Physics-Informed Neural Network (TE-PINN) designed for accurate quaternion-based orientation estimation in high-dynamic environments, particularly within the field of robotics.
Golroudbari, Arman Asgharpoor
core  

Determining pressure from velocity via physics-informed neural network

This paper describes a physics-informed neural network (PINN) for determining pressure from velocity where the Navier-Stokes (NS) equations are incorporated as a physical constraint, but the boundary condition is not explicitly imposed.
王士召, Lin, Xiangqi, Wu, Fan, Liu, Tianshu, Wang, Shizhao, 吴凡, Cai, Zemin, Liu, Yun   +7 more
core   +1 more source