Results 61 to 70 of about 16,533 (261)

Predicting micro-bubble dynamics with semi-physics-informed deep learning

AIP Advances, 2022
Utilizing physical information to improve the performance of the conventional neural networks is becoming a promising research direction in scientific computing recently.
Hanfeng Zhai, Quan Zhou, Guohui Hu
doaj   +1 more source

Evaluation of Digital Technologies for Home‐Based Assessment in People With Amyotrophic Lateral Sclerosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Digital technologies hold promise for transforming healthcare by enhancing personalized treatments and offer valuable opportunities to improve patient care. Here, we evaluated several novel, self‐administered, home‐based, digital endpoints for their association with corresponding conventional standard clinical measures (primary) in ...
Arne Mueller, Vanessa Vallejo, Mónica Povedano Panadés, Orla Hardiman, Véronique Danel‐Brunaud, Senda Ajroud‐Driss, Philippe Couratier, Jeremy Shefner, Anna Gokey, Mike DiCesare, Erin Lennox, Jens Praestgaard, Lucie Brujin, Peggy Allred, Ram Miller   +14 more
wiley   +1 more source

Sex‐Stratified Association of Regional Dopamine Transporter Binding With Disease Progression in Amyotrophic Lateral Sclerosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To clarify the clinical relevance of dopamine transporter single‐photon emission computed tomography (DAT‐SPECT) abnormalities in amyotrophic lateral sclerosis (ALS), with a prespecified focus on sex‐stratified associations with disease progression and short‐term prognosis.
Tomoya Kawazoe, Keizo Sugaya, Kentaro Hayashi, Keiko Hino, Yasuhiro Nakata, Kota Bokuda, Toshio Shimizu, Kazushi Takahashi   +7 more
wiley   +1 more source

Physics-Informed Neural Networks for Quantum Control

Physical Review Letters
Quantum control is a ubiquitous research field that has enabled physicists to delve into the dynamics and features of quantum systems, delivering powerful applications for various atomic, optical, mechanical, and solid-state systems. In recent years, traditional control techniques based on optimization processes have been translated into efficient ...
Ariel Norambuena, Marios Mattheakis, Francisco J. González, Raúl Coto   +3 more
openaire   +3 more sources

White Matter Microstructural Abnormalities in Neonatal Onset Genetic Epilepsy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Recent evidence indicates that epilepsy is associated with abnormal white matter. If seizures alter white matter, then the impact upon network function, epileptogenesis, and cognition could be pronounced in neonates undergoing rapid developmental myelination. Neonates with epilepsy due to nonstructural genetic causes provide a unique
Amanda G. Sandoval Karamian, Tianjia Zhu, Hao Huang, Shavonne L. Massey, Mark P. Fitzgerald, Darshana Parikh, Arastoo Vossough, Ingo Helbig, Nicholas S. Abend   +8 more
wiley   +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

Artificial Intelligence in Systemic Sclerosis: Clinical Applications, Challenges, and Future Directions

Arthritis Care &Research, EarlyView.
Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality.
Cristiana Sieiro Santos, José Miguel Sequí‐Sabater, Diego Benavent   +2 more
wiley   +1 more source

Physics Informed Neural Networks for Modeling Large-Scale Wind Driven Ocean Circulation

IEEE Access
This study investigates the application of the physics informed neural network as a meshfree collocation method for approximating solutions to large-scale wind driven ocean circulation models.
Boohyun An, Mohammad Z. Shanti, Chan Yeob Yeun, Ernesto Damiani, Sungmun Lee, Tae-Yeon Kim   +5 more
doaj   +1 more source

Physics-informed deep learning for incompressible laminar flows

Theoretical and Applied Mechanics Letters, 2020
: Physics-informed deep learning has drawn tremendous interest in recent years to solve computational physics problems, whose basic concept is to embed physical laws to constrain/inform neural networks, with the need of less data for training a reliable ...
Chengping Rao, Hao Sun, Yang Liu
doaj   +1 more source

Robust Variational Physics-Informed Neural Networks

Computer Methods in Applied Mechanics and Engineering
We introduce a Robust version of the Variational Physics-Informed Neural Networks method (RVPINNs). As in VPINNs, we define the quadratic loss functional in terms of a Petrov-Galerkin-type variational formulation of the PDE problem: the trial space is a (Deep) Neural Network (DNN) manifold, while the test space is a finite-dimensional vector space ...
Rojas, Sergio, Maczuga, Paweł, Muñoz-Matute, Judit, Pardo, David, Paszyński, Maciej   +4 more
openaire   +3 more sources