Results 61 to 70 of about 6,454,505 (250)

Neural Network Models for Predicting Magnetization Surface Switched Reluctance Motor: Classical, Radial Basis Function, and Physics-Informed Techniques

IEEE Access
Neural networks have increasingly been utilized in electric drive systems to enhance modeling, control, and optimization. These data-driven techniques enable accurate predictions of complex nonlinear behaviors, including the magnetization characteristics
Galina Demidova, Jackson J. Justo, Dmitry Lukichev, Nikolai Poliakov, Alecksey Anuchin   +4 more
doaj   +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

Determinants of Body Mass Index in Early Systemic Sclerosis: Implications for Nutritional Risk Stratification

Arthritis Care &Research, EarlyView.
Objective Gastrointestinal (GI) involvement can lead to malnutrition in patients with systemic sclerosis (SSc). Body mass index (BMI) remains the most widely used marker to screen nutritional status. We aimed to identify predictors of lower BMI in patients with SSc. Methods Patients with SSc from a prospective US cohort meeting 2013 American College of
Ali Y. Ayla, Meng Zhang, Bingrui Chen, Claudia Pedroza, Brian Skaug, Maureen Mayes, Michael Hughes, Shervin Assassi, Zsuzsanna H. McMahan   +8 more
wiley   +1 more source

Transient Stability Analysis with Physics-Informed Neural Networks [PDF]

, 2023
We explore the possibility to use physics-informed neural networks to drastically accelerate the solution of ordinary differential-algebraic equations that govern the power system dynamics.
Chatzivasileiadis, Spyros, Stiasny, Jochen, Misyris, Georgios S.   +2 more
core  

Clinical Validation of Artificial Intelligence (AI)‐based Cartilage Segmentation Predicting Knee Replacement

Arthritis Care &Research, Accepted Article.
Objective For cartilage morphology to serve as a scalable endpoint in clinical trials, analyses should be performed automatically without human interaction. To clinically validate artificial intelligence (AI)‐based analysis, we studied cartilage loss from MRI prior to knee replacement.
Felix Eckstein, David J. Hunter, C. Kent Kwoh, Wolfgang Wirth   +3 more
wiley   +1 more source

Hamiltonian learning via inverse physics-informed neural networks

Physical Review Research
Hamiltonian learning (HL), enabling precise estimation of system parameters and underlying dynamics, plays a critical role in characterizing quantum systems.
Jie Liu, Xin Wang
doaj   +1 more source

Beyond Visual Scoring: Computational Computed Tomography Analysis for High‐Resolution Computed Tomography–Based Quantification of Interstitial Lung Disease in Inflammatory Rheumatic Disease

Arthritis Care &Research, EarlyView.
Interstitial lung disease (ILD) is a significant cause of morbidity and mortality in patients with inflammatory rheumatic disorders (IRDs). High‐resolution computed tomography (HRCT) is widely considered the gold standard for the noninvasive assessment of ILD; however, its interpretation is constrained by substantial interobserver variability and the ...
Alexander Pfeil, Tobias Hoffmann, Ulf Teichgräber, Diane Renz, Denise Beckmann, Gunter Wolf, Nicole Schadewaldt, Bianca Lassen‐Schmidt   +7 more
wiley   +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

Development in metal multiaxial fatigue life prediction based on physics-informed neural network

Jixie qiangdu
The research on multiaxial fatigue life prediction of materials is one of the critical elements in ensuring the structural integrity of components. In recent years, machine learning, especially neural networks, has been widely applied in fatigue life ...
ZHANG Zhuanli, SUN Xingyue, CHEN Xu
doaj  

Innovative Applications of Physics-Informed Neural Networks

, 2023
Innovative Applications of Physics-Informed Neural ...
Vikash Malhotra
core   +1 more source