LaSDI: parametric latent space dynamics identification (Q2674132)

scientific article; zbMATH DE number 7589853

Language	Label	Description	Also known as
English	LaSDI: parametric latent space dynamics identification	scientific article; zbMATH DE number 7589853

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instance of

scholarly article

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title

LaSDI: parametric latent space dynamics identification (English)

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Computer Methods in Applied Mechanics and Engineering

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publication date

22 September 2022

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full work available at URL

https://arxiv.org/abs/2203.02076

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zbMATH Keywords

reduced order models

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latent space learning

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principle orthogonal decomposition

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nonlinear manifold solution representation

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nonlinear dynamical system

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describes a project that uses

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A Schur method for balanced-truncation model reduction

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A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder

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Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders

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Reduced order models for Lagrangian hydrodynamics

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SNS: A Solution-Based Nonlinear Subspace Method for Time-Dependent Model Order Reduction

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Space--Time Least-Squares Petrov--Galerkin Projection for Nonlinear Model Reduction

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Conservative model reduction for finite-volume models

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Domain-decomposition least-squares Petrov-Galerkin (DD-LSPG) nonlinear model reduction

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Gradient-based constrained optimization using a database of linear reduced-order models

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Variational multiscale proper orthogonal decomposition: Navier‐stokes equations

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Space-time reduced order model for large-scale linear dynamical systems with application to Boltzmann transport problems

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Component-wise reduced order model lattice-type structure design

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

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Deep learning in fluid dynamics

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A machine learning approach for efficient uncertainty quantification using multiscale methods

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A paradigm for data-driven predictive modeling using field inversion and machine learning

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Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

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

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Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks

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A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs

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Projection-based model reduction of dynamical systems using space-time subspace and machine learning

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Data-driven discovery of coordinates and governing equations

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Dynamic mode decomposition of numerical and experimental data

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Dynamic mode decomposition with control

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Applications of the dynamic mode decomposition

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Q2957673

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Dynamic Mode Decomposition and Its Variants

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Multiresolution dynamic mode decomposition

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Higher Order Dynamic Mode Decomposition

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Randomized Dynamic Mode Decomposition

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Data-driven operator inference for nonintrusive projection-based model reduction

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Lift \& learn: physics-informed machine learning for large-scale nonlinear dynamical systems

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Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms

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Sampling Low-Dimensional Markovian Dynamics for Preasymptotically Recovering Reduced Models from Data with Operator Inference

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Non-intrusive data-driven model reduction for differential-algebraic equations derived from lifting transformations

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Turbulence and the dynamics of coherent structures. I. Coherent structures

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Discovering governing equations from data by sparse identification of nonlinear dynamical systems

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A family of embedded Runge-Kutta formulae

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Identifiers

Mathematics Subject Classification ID

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10.1016/J.CMA.2022.115436

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Sitelinks

Mathematics(1 entry)

mardi Publication:2674132