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2101.11513
Cited By

State estimation with limited sensors -- A deep learning based approach

v1v2 (latest)

State estimation with limited sensors -- A deep learning based approach

Journal of Computational Physics (JCP), 2021

27 January 2021

ArXiv (abs)PDF HTML

Papers citing "State estimation with limited sensors -- A deep learning based approach"

7 / 7 papers shown

EngineBench: Flow Reconstruction in the Transparent Combustion Chamber
III Optical Engine

EngineBench: Flow Reconstruction in the Transparent Combustion Chamber III Optical Engine

Samuel J. Baker

Michael A. Hobley

Felix C. P. Leach

274

3

0

05 Jun 2024

RecFNO: a resolution-invariant flow and heat field reconstruction method
from sparse observations via Fourier neural operator

RecFNO: a resolution-invariant flow and heat field reconstruction method from sparse observations via Fourier neural operatorInternational journal of thermal sciences (Int. J. Therm. Sci.), 2023

176

45

0

20 Feb 2023

FR3D: Three-dimensional Flow Reconstruction and Force Estimation for
Unsteady Flows Around Extruded Bluff Bodies via Conformal Mapping Aided
Convolutional Autoencoders

FR3D: Three-dimensional Flow Reconstruction and Force Estimation for Unsteady Flows Around Extruded Bluff Bodies via Conformal Mapping Aided Convolutional AutoencodersInternational Journal of Heat and Fluid Flow (IJHFF), 2023

Ali Girayhan Ozbay

252

3

0

03 Feb 2023

Energy networks for state estimation with random sensors using sparse
labels

Energy networks for state estimation with random sensors using sparse labels

232

0

0

12 Mar 2022

Deep learning fluid flow reconstruction around arbitrary two-dimensional
objects from sparse sensors using conformal mappings

Deep learning fluid flow reconstruction around arbitrary two-dimensional objects from sparse sensors using conformal mappingsAIP Advances (AIP Adv.), 2022

Ali Girayhan Ozbay

168

18

0

08 Feb 2022

A deep learning based surrogate model for stochastic simulators

A deep learning based surrogate model for stochastic simulatorsProbabilistic Engineering Mechanics (PEM), 2021

301

21

0

24 Oct 2021

Machine learning based digital twin for stochastic nonlinear
multi-degree of freedom dynamical system

Machine learning based digital twin for stochastic nonlinear multi-degree of freedom dynamical systemProbabilistic Engineering Mechanics (PEM), 2021

173

21

0

29 Mar 2021

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