Results 101 to 110 of about 8,084,029 (292)

Extended multiphysics-informed neural network for conjugate heat transfer problems

Physics-informed neural networks have gained prominence as an innovative framework that combines machine learning and physics knowledges to solve complex physical problems.
Lee, Seungchul, Lee, Jongmok, Lee, Anna, Choi, Ho, Shin, Seungmin, Park, Bumsoo   +5 more
core   +1 more source

Modeling chemical kinetics of geopolymers using physics informed neural network [PDF]

Using a physics informed neural network for the analysis of geopolymers as an alternate material for cement can be a viable approach, as neural networks are capable of modeling complex, nonlinear relationships in data, which can be beneficial for ...
Abraham, Blesso, Sudhakar, Thirumalaivasal Devanathan   +1 more
core   +1 more source

Automated Hand Flexor Tendon–Thickness Measurement in Systemic Sclerosis

Arthritis Care &Research, EarlyView.
Objective Systemic sclerosis (SSc) can affect flexor tendons, contributing to hand function problems and reduced quality of life. Tendon changes are currently assessed with ultrasonography and measured manually, a time‐consuming process prone to interobserver variability.
Mark Greveling, Ties van het Erve, Yvette van de Berg, Simon Mastbergen, Julia Spierings   +4 more
wiley   +1 more source

Incidence, Risk Factors and Management of Adverse Events in Contemporary Real‐World Care of Children with Juvenile Idiopathic Arthritis

Arthritis Care &Research, Accepted Article.
Objective We describe the frequency, risk factors, severity, and management of actionable and serious adverse events (AAE and SAE) in children with newly diagnosed Juvenile Idiopathic Arthritis (JIA) in Canada. Methods We enrolled patients within 3 months of JIA diagnosis in the Canadian Alliance of Pediatric Rheumatology Investigators (CAPRI) Registry,
Bashayer Alnuaimi, Roberta A. Berard, Jean‐Philippe Proulx‐Gauthier, Heinrike Schmeling, Michelle Batthish, Gilles Boire, Adam M. Huber, Dax G. Rumsey, Jaime Guzman, Gaëlle Chédeville, The CAPRI registry Investigators   +10 more
wiley   +1 more source

Generative Artificial Intelligence in Clinical Practice: How Should Rheumatologists Evaluate and Monitor Emerging Tools?

Arthritis Care &Research, EarlyView.
Generative artificial intelligence (AI) is increasingly being used in rheumatology practice, to assist with tasks such as documentation, record summarization, and clinical decision support. Rheumatologists need a practical approach to determining what evidence and oversight each application requires.
Koushan Kouranloo, Mrinalini Dey, Jinoos Yazdany   +2 more
wiley   +1 more source

Multi-level physics informed deep learning for solving partial differential equations in computational structural mechanics

Communications Engineering
Physics-informed neural network has emerged as a promising approach for solving partial differential equations. However, it is still a challenge for the computation of structural mechanics problems since it involves solving higher-order partial ...
Weiwei He, Jinzhao Li, Xuan Kong, Lu Deng   +3 more
doaj   +1 more source

Observer‐Based Adaptive Event‐Triggered Tracking Control for Fuzzy TS Systems With Premise Mismatch

International Journal of Adaptive Control and Signal Processing, EarlyView.
This paper presents an adaptive logistic event‐triggered observer‐based tracking controller for Takagi‐Sugeno fuzzy systems under constrained inputs and network delays. Leveraging a hybrid LMI and Secretary Bird Optimization approach, this strategy significantly minimizes communication overhead and computational burden while ensuring optimal reference ...
Oussama Djadane, Salama Makni, Mourad Kchaou, Ahmed El Hajjaji   +3 more
wiley   +1 more source

Physics-Informed Neural Networks for Power Systems

, 2020
This paper introduces for the first time, to our knowledge, a framework for physics-informed neural networks in power system applications. Exploiting the underlying physical laws governing power systems, and inspired by recent developments in the field ...
Venzke, Andreas, Chatzivasileiadis, Spyros, Spyros Chatzivasileiadis, George S. Misyris, Andreas Venzke, Misyris, Georgios   +5 more
core   +1 more source

Space Correlation Constrained Physics Informed Neural Network for Seismic Tomography

Journal of Geophysical Research: Machine Learning and Computation
Physics‐informed neural networks (PINNs) integrate physical constraints with neural architectures and leverage their nonlinear fitting capabilities to solve complex inverse problems.
Yonghao Wang, Zhuo Jia, Bowu Jiang, Wenkai Lu   +3 more
doaj   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +1 more source