Branched latent neural maps (Q6118560)

scientific article; zbMATH DE number 7822145

Language	Label	Description	Also known as
English	Branched latent neural maps	scientific article; zbMATH DE number 7822145

Statements

instance of

scholarly article

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title

Branched latent neural maps (English)

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arXiv ID

2308.02599

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DOI

10.1016/j.cma.2023.116499

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author

Matteo Salvador

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Alison Lesley Marsden

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published in

Computer Methods in Applied Mechanics and Engineering

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publication date

21 March 2024

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full work available at URL

https://arxiv.org/abs/2308.02599

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Mathematics Subject Classification ID

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branched latent neural maps

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scientific machine learning

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numerical simulations

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cardiac electrophysiology

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congenital heart disease

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MaRDI profile type

MaRDI publication profile

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OpenAlex ID

W4387443414

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cites work

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Non-intrusive reduced order modeling of nonlinear problems using neural networks

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Machine learning of multiscale active force generation models for the efficient simulation of cardiac electromechanics

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A machine learning method for real-time numerical simulations of cardiac electromechanics

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A comprehensive and biophysically detailed computational model of the whole human heart electromechanics

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Machine learning for fast and reliable solution of time-dependent differential equations

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Mathematical Modelling of the Human Cardiovascular System

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Mathematical cardiac electrophysiology

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Modeling cardiac muscle fibers in ventricular and atrial electrophysiology simulations

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A cardiac electromechanical model coupled with a lumped-parameter model for closed-loop blood circulation

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3D-0D closed-loop model for the simulation of cardiac biventricular electromechanics

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Q5270493

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Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

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POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition

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Non-linear manifold reduced-order models with convolutional autoencoders and reduced over-collocation method

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Reliable extrapolation of deep neural operators informed by physics or sparse observations

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Scientific machine learning through physics-informed neural networks: where we are and what's next

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nPINNs: nonlocal physics-informed neural networks for a parametrized nonlocal universal Laplacian operator. Algorithms and applications

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A metalearning approach for physics-informed neural networks (PINNs): application to parameterized PDEs

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Multifidelity deep operator networks for data-driven and physics-informed problems

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Sitelinks

Mathematics(1 entry)

mardi Publication:6118560