Results 61 to 70 of about 8,084,029 (292)
Physics‐Informed Neural Network for Magnetization Distribution Estimation
IET Electric Power ApplicationsAccurately estimating the magnetization distribution in permanent magnets is critical for optimising their performance in various applications, such as electric motors, generators and magnetic sensors, where precise magnetic field control is essential. A
Zhi Gong, Zuqi Tang, Abdelkader Benabou
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A Physics-Informed Neural Network Approach for Nearfield Acoustic Holography
Sensors, 2021 In this manuscript, we describe a novel methodology for nearfield acoustic holography (NAH). The proposed technique is based on convolutional neural networks, with autoencoder architecture, to reconstruct the pressure and velocity fields on the surface ...
Marco Olivieri +3 more
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Preconditioning for Physics-Informed Neural Networks
CoRRPhysics-informed neural networks (PINNs) have shown promise in solving various partial differential equations (PDEs). However, training pathologies have negatively affected the convergence and prediction accuracy of PINNs, which further limits their practical applications.
Songming Liu +6 more
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Probing optimisation in physics-informed neural networks
CoRR, 2023 Accepted at the ICLR 2023 Workshop on Physics for Machine ...
Nayara Fonseca +2 more
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Going viral − Virus‐targeting nanobodies for research, diagnostics and therapy
FEBS Letters, EarlyView.The increasing prevalence of viral diseases represents a major threat for global human health. Nanobodies, small binding fragments derived from camelid heavy‐chain antibodies, offer versatile tools for viral research, diagnostics, and antiviral therapy.
Domenic Ebert, Ulrich Rothbauer
wiley +1 more source
A Physics-Informed Neural Network-Based Waveguide Eigenanalysis
IEEE AccessThis work presents a deep neural network (DNN)-based approach for identifying the modal field distributions of closed non-radiating waveguides. Specifically, physics-informed neural networks (PINNs) are used to solve the Helmholtz partial differential ...
Md Rayhan Khan +3 more
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Directed evolution of enzymes at the crossroads of tradition and innovation
FEBS Open Bio, EarlyView.An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova +2 more
wiley +1 more source
GPINN: Physics-informed Neural Network with Graph Embedding
, 2023 This work proposes a Physics-informed Neural Network framework with Graph Embedding (GPINN) to perform PINN in graph, i.e. topological space instead of traditional Euclidean space, for improved problem-solving efficiency.
Miao, Yuyang, Li, Haolin
core
Physics informed neural network for charged particles surrounded by conductive boundaries
Scientific Reports, 2023 Molecular dynamics of charged particles in porous conductive media have received considerable attention in recent years due to their application in cutting-edge technologies such as batteries and supercapacitors.
Fatemeh Hafezianzade +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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