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Transformers for modeling physical systems (Q6055222)

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scientific article; zbMATH DE number 7743741

Language	Label	Description	Also known as
English	Transformers for modeling physical systems	scientific article; zbMATH DE number 7743741

Statements

scholarly article

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Transformers for modeling physical systems (English)

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Nicholas Geneva

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Nicholas Zabaras

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Neural Networks

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publication date

28 September 2023

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full work available at URL

https://arxiv.org/abs/2010.03957

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zbMATH Keywords

transformers

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deep learning

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self-attention

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physical systems

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Koopman-based embedding

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surrogate modeling

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MaRDI profile type

MaRDI publication profile

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Structured Bayesian Gaussian process latent variable model: applications to data-driven dimensionality reduction and high-dimensional inversion

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Multi-output separable Gaussian process: towards an efficient, fully Bayesian paradigm for uncertainty quantification

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Approximation by superpositions of a sigmoidal function

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Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning

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Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks

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Multilayer feedforward networks are universal approximators

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Hamiltonian Systems and Transformation in Hilbert Space

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Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control

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Data-driven spectral analysis of the Koopman operator

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Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator

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Linearly Recurrent Autoencoder Networks for Learning Dynamics

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems

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Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification

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The neural particle method - an updated Lagrangian physics informed neural network for computational fluid dynamics

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The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations

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Multi-level convolutional autoencoder networks for parametric prediction of spatio-temporal dynamics

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Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification

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Identifiers

zbMATH Open document ID

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Mathematics Subject Classification ID

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zbMATH DE Number

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10.1016/J.NEUNET.2021.11.022

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Sitelinks

Mathematics(1 entry)

mardi Publication:6055222

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