Results 61 to 70 of about 80,482 (270)

Vestibular Patient Journey: Insights From Vestibular Disorders Association (VeDA) Registry

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Vestibular symptoms impose a high burden of disability. Understanding real‐world diagnostic and treatment pathways can identify care gaps and guide interventions. We aimed to characterize symptom profiles, diagnostic trends, provider involvement, and treatment patterns in vestibular disorders.
Ali Rafati, Sami I. Nassar, Shaun A. Nguyen, Patricia L. Gerend, Michael C. Schubert, Michael T. Teixido, Frederick A. Godley, Joel A. Goebel, Cynthia A. Ryan, Habib G. Rizk, Amir Kheradmand   +10 more
wiley   +1 more source

SPIKANs: separable physics-informed Kolmogorov–Arnold networks

Machine Learning: Science and Technology
Physics-Informed Neural Networks (PINNs) have emerged as a promising method for solving partial differential equations (PDEs) in scientific computing.
Bruno Jacob, Amanda A Howard, Panos Stinis   +2 more
doaj   +1 more source

Online Continual Physics-Informed Learning for Quadrotor State Estimation Under Wind-Induced Disturbances

Aerospace
Accurate state estimation for quadrotors under wind-induced disturbances remains a critical challenge in dynamic outdoor environments. Existing model-based and data-driven approaches often struggle with real-time adaptation and catastrophic forgetting ...
Yanhui Liu, Shuopeng Wang, Junhua Shi, Lina Hao   +3 more
doaj   +1 more source

Physics-Informed Deep Neural Networks for Transient Electromagnetic Analysis

IEEE Open Journal of Antennas and Propagation, 2020
In this paper, we propose a deep neural network based model to predict the time evolution of field values in transient electrodynamics. The key component of our model is a recurrent neural network, which learns representations of long-term spatial ...
Oameed Noakoasteen, Shu Wang, Zhen Peng, Christos Christodoulou   +3 more
doaj   +1 more source

h-Analysis and data-parallel physics-informed neural networks

Scientific Reports, 2023
We explore the data-parallel acceleration of physics-informed machine learning (PIML) schemes, with a focus on physics-informed neural networks (PINNs) for multiple graphics processing units (GPUs) architectures. In order to develop scale-robust and high-
Paul Escapil-Inchauspé, Gonzalo A. Ruz
doaj   +1 more source

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting [PDF]

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age.
Andrejková G., Ashmall J., Bergstra J., E. Camporeale, Fasshauer G. E., Gelman A., Goodfellow I., Murphy K. P., Parnowski A., Pedregosa F., Pesnell W. D., Russell S. J., Semeniv O., Stepanova M., Stringer G., Sutton R. S., Turner D., Valach F., Vapnik V., Vega‐Jorquera P.   +19 more
core   +1 more source

Quasi-random physics-informed neural networks

Neurocomputing
Physics-informed neural networks have shown promise in solving partial differential equations (PDEs) by integrating physical constraints into neural network training, but their performance is sensitive to the sampling of points. Based on the impressive performance of quasi Monte-Carlo methods in high dimensional problems, this paper proposes Quasi ...
Tianchi Yu, Ivan V. Oseledets
openaire   +2 more sources

Normal‐Appearing White Matter Injury Mediates Chronic Deep Venous Hypoxia and Disease Progression in Multiple Sclerosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To explore how cerebral hypoxia and Normal‐Appearing White Matter (NAWM) integrity affect MS lesion burden and clinical course. Methods Seventy‐nine MS patients, including 13 clinically isolated syndrome (CIS) patients and 66 relapsing–remitting multiple sclerosis (RRMS) patients, and 44 healthy controls (HCs) were recruited from ...
Xinli Wang, Huiying Wang, Zhizheng Zhuo, Ai Guo, Ke Lv, Decai Tian, Chao Chai, Yunyun Duan, Shuang Xia   +8 more
wiley   +1 more source

MT-PINNs: multi-term physics-informed neural networks for solving initial boundary value problems of 2D and 3D nonlinear telegraph equations

Boundary Value Problems
The nonlinear telegraph equation appears in a variety of engineering and science problems. This paper presents a deep learning algorithm termed multi-term physics-informed neural networks to resolve initial boundary value problems of 2D and 3D hyperbolic
Alemayehu Tamirie Deresse, Alemu Senbeta Bekela, Tamirat Temesgen Dufera   +2 more
doaj   +1 more source

Comparing the Effect of Semi‐Immersive Virtual Reality, Computerized Cognitive Training, and Traditional Rehabilitation on Cognitive Function in Multiple Sclerosis: A Randomized Clinical Trial

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Cognitive impairment is a common non‐motor symptom in Multiple Sclerosis (MS), negatively affecting autonomy and Quality of Life (QoL). Innovative rehabilitation strategies, such as semi‐immersive virtual reality (VR) and computerized cognitive training (CCT), may offer advantages over traditional cognitive rehabilitation (TCR ...
Maria Grazia Maggio, Desiree Latella, Lilla Bonanno, Paulo Cezar Rocha dos Santos, Marcos Maldonado‐Díaz, Meir Plotnik, Rosaria De Luca, Angelo Quartarone, Rocco Salvatore Calabrò   +8 more
wiley   +1 more source