An extended physics informed neural network for preliminary analysis of parametric optimal control problems (Q6104895)

scientific article; zbMATH DE number 7703997

Language	Label	Description	Also known as
English	An extended physics informed neural network for preliminary analysis of parametric optimal control problems	scientific article; zbMATH DE number 7703997

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instance of

scholarly article

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title

An extended physics informed neural network for preliminary analysis of parametric optimal control problems (English)

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28 June 2023

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full work available at URL

https://arxiv.org/abs/2110.13530

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zbMATH Keywords

physics-informed neural network

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deep learning

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optimal control problem

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parametric partial differential equations

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MaRDI profile type

Publication

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cites work

Analytical solutions of boundary values problem of 2D and 3D Poisson and biharmonic equations by homotopy decomposition method

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Optimal flow control for Navier–Stokes equations: drag minimization

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Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities

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Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks

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Adaptive activation functions accelerate convergence in deep and physics-informed neural networks

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NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations

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\textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition

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Trends in PDE constrained optimization

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All-optical machine learning using diffractive deep neural networks

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Q5601627

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PPINN: parareal physics-informed neural network for time-dependent PDEs

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A MULTI-FIDELITY NEURAL NETWORK SURROGATE SAMPLING METHOD FOR UNCERTAINTY QUANTIFICATION

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Reduced basis approximation of parametrized optimal flow control problems for the Stokes equations

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

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Optimal control of Navier-Stokes equations by Oseen approximation

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Numerical Approximation of a Control Problem for Advection-Diffusion Processes

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Q3521017

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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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Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

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Model Reduction for Parametrized Optimal Control Problems in Environmental Marine Sciences and Engineering

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Reduced Order Methods for Parametrized Non-linear and Time Dependent Optimal Flow Control Problems, Towards Applications in Biomedical and Environmental Sciences

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Q3558349

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B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data

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published in

Computers & Mathematics with Applications

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Identifiers

arXiv ID

2110.13530

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Mathematics Subject Classification ID

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10.1016/J.CAMWA.2023.05.004

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Sitelinks

Mathematics(1 entry)

mardi Publication:6104895