Results 61 to 70 of about 18,341 (302)
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 +3 more
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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
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Conformalized Physics-Informed Neural Networks
CoRRPhysics-informed neural networks (PINNs) are an influential method of solving differential equations and estimating their parameters given data. However, since they make use of neural networks, they provide only a point estimate of differential equation parameters, as well as the solution at any given point, without any measure of uncertainty. Ensemble
Lena Podina +2 more
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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 +10 more
wiley +1 more source
Boundary Value ProblemsThe 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 +2 more
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Quasi-random physics-informed neural networks
NeurocomputingPhysics-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
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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 +8 more
wiley +1 more source
Physics-informed neural networks for hydraulic transient analysis in pipeline systems
, 2022 Physics-informed neural networks for hydraulic transient analysis in pipeline ...
J Ye (8430171) +3 more
core
, 2023 Bridging Physics and AI: The Power of Physics-Informed Neural Networks (PINNs) in Experimental ...
Kamran Mathias
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Theory-informed neural networks for particle physics
Machine Learning: Science and TechnologyWe present a theory-informed reinforcement-learning framework that recasts the combinatorial assignment of final-state particles in hadron collider events as a Markov decision process.
Barry M Dillon, Michael Spannowsky
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