Results 71 to 80 of about 6,454,505 (250)

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

Physics-informed neural networks for tsunami inundation modeling [PDF]

We use physics-informed neural networks for solving the shallow-water equations for tsunami modeling. Physics-informed neural networks are an optimization based approach for solving differential equations that is completely meshless.
Cardoso-Bihlo, Elsa, Bihlo, Alex, Brecht, Rüdiger   +2 more
core   +1 more source

Physics-informed neural networks for granular flows modelling

Mechanics & Industry
Physics-Informed Neural Networks (PINNs) have recently emerged as a powerful framework for solving forward and inverse problems involving partial differential equations, by embedding physical laws directly into the training process of neural networks.
Baldoni Barbara, Delcey Mickaël, Cheny Yoann, Gans Adrien, Jenny Mathieu, Kiesgen de Richter Sébastien   +5 more
doaj   +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

Physics-Informed Neural Networks for Structural Mechanics and Construction: Modeling the Deflection of a Single-Span Beam Physics-Informed Neural Networks

Железобетонные конструкции
This article presents the development and analysis of a Physics-Informed Neural Network (PINN) model for calculating the deflection of a simply supported beam under uniformly distributed load.
F. N. Zakharov, Qian Jie, Xu Yi
doaj   +1 more source

Structural analysis of 2D frame structures using Physics Informed Neural Networks: Calculation files and result data

Within the present study, a framework for Physics Informed Neural Networks (PINN) is formulated for the analysis of frame structures in two dimensions.
Most, Thomas, Könke, Carsten, Deckert, Felix, Lippold, Lukas   +3 more
core   +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

Multi-Step Physics-Informed Deep Operator Neural Network for Directly Solving Partial Differential Equations

Applied Sciences
This paper establishes a method for solving partial differential equations using a multi-step physics-informed deep operator neural network. The network is trained by embedding physics-informed constraints.
Jing Wang, Yubo Li, Anping Wu, Zheng Chen, Jun Huang, Qingfeng Wang, Feng Liu   +6 more
doaj   +1 more source

Physics Informed Symbolic Networks

, 2022
We introduce Physics Informed Symbolic Networks (PISN) which utilize physics-informed loss to obtain a symbolic solution for a system of Partial Differential Equations (PDE).
Jadhav, Vishal, Vig, Lovekesh, Deodhar, Anirudh, Karande, Shirish, Majumdar, Ritam, Runkana, Venkataramana   +5 more
core  

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