Results 51 to 60 of about 2,349 (245)

A Physics-Informed Automatic Neural Network Generation Framework for Emerging Device Modeling

Micromachines, 2023
With the rapid development of semiconductor technology, traditional equation-based modeling faces challenges in accuracy and development time. To overcome these limitations, neural network (NN)-based modeling methods have been proposed.
Guangxin Guo, Hailong You, Cong Li, Zhengguang Tang, Ouwen Li   +4 more
doaj   +1 more source

Bayesian Physics Informed Neural Networks for Data Assimilation and Spatio-Temporal Modelling of Wildfires

, 2023
We apply the Physics Informed Neural Network (PINN) to the problem of wildfire fire-front modelling. We use the PINN to solve the level-set equation, which is a partial differential equation that models a fire-front through the zero-level-set of a level ...
Bolt, Andrew, Andrew Bolt, Sanderson, Conrad, Kuhnert, Petra, James Hilton, Dabrowski, Joel Janek, MacKinlay, Daniel, Daniel MacKinlay, Carolyn Huston, Huston, Carolyn, Conrad Sanderson, Daniel Edward Pagendam, Hilton, James, Joel Janek Dabrowski, Petra Kuhnert, Pagendam, Daniel Edward   +15 more
core   +1 more source

PND: Physics-informed neural-network software for molecular dynamics applications

SoftwareX, 2021
We have developed PND, a differential equation solver software based on a physics-informed neural network (PINN) for molecular dynamics simulators. Based on automatic differentiation technique provided by PyTorch, our software allows users to flexibly ...
Taufeq Mohammed Razakh, Beibei Wang, Shane Jackson, Rajiv K. Kalia, Aiichiro Nakano, Ken-ichi Nomura, Priya Vashishta   +6 more
doaj   +1 more source

Intelligent Maintenance Review for Robots: Multimodal Information, Deep Diagnosis and Embodied Artificial Intelligence

Advanced Robotics Research, EarlyView.
This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao, Hongbo Wang, Naipeng Li, Shuo Wang, Yaguo Lei, Tonghai Wu, Junyi Cao   +6 more
wiley   +1 more source

A Residual Physics-Informed Neural Network Approach for Identifying Dynamic Parameters in Swing Equation-Based Power Systems

Energies
Several challenges hinder accurate and physically consistent dynamic parameter estimation in power systems, particularly under scenarios involving limited measurements, strong system nonlinearity, and high variability introduced by renewable integration.
Jiani Zeng, Xianglong Li, Hanqi Dai, Lu Zhang, Weixian Wang, Zihan Zhang, Shengxin Kong, Liwen Xu   +7 more
doaj   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Physics-Guided Machine Learning with Flowing Material Balance Integration: A Novel Approach for Reliable Production Forecasting and Well Performance Analytics

Energies
Reliable production forecasting is a critical task for evaluating asset valuation and commercial performance in oil and gas reservoirs. Conventional short-term forecasting methods, such as Arps’ decline curve analysis, rely on simple mathematical curve ...
Eghbal Motaei, Tarek Ganat, Hai T. Nguyen   +2 more
doaj   +1 more source

How Advanced Artificial Intelligence Technologies Shape Drug–Drug and Drug–Target Interaction Modeling

Advanced Science, EarlyView.
This review explores the convergence of artificial intelligence technologies in modeling drug–drug and drug–target interactions. By evaluating advanced feature engineering, architectural innovations, and learning paradigms reveals shared evolutionary trends and critical challenges, such as cold‐start settings and shortcut learning.
Xin Sun, Tong Wang
wiley   +1 more source

FO-PINN: A First-Order formulation for Physics-Informed Neural Networks

Engineering Analysis with Boundary Elements
Physics-Informed Neural Networks (PINNs) are a class of deep learning neural networks that learn the response of a physical system without any simulation data, and only by incorporating the governing partial differential equations (PDEs) in their loss function.
Rini Jasmine Gladstone, Mohammad Amin Nabian, N. Sukumar, Ankit Srivastava, Hadi Meidani   +4 more
openaire   +2 more sources

Machine Learning‐Driven Prediction of Microplastic Aging Processes and Environmental Risk Assessment Across Multi‐Media Systems

Advanced Science, EarlyView.
This perspective proposes a cohesive machine learning strategy to decode microplastic aging. It advocates for Federated Learning to dismantle global data silos and introduces the TRACE framework (TRansport, Aging, Corona, Ecotoxicity). By integrating physics‐informed modeling with causal discovery, this approach bridges the laboratory‐field gap to ...
Yaping Lyu, Xinran Qiu, Xing Li, Tianhuan Yang, Xuetao Guo, Hao Qiu, Peng Zhang   +6 more
wiley   +1 more source