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State estimation with limited sensors -- A deep learning based approach
v1v2 (latest)

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

27 January 2021
Y. Kumar
Pranav Bahl
S. Chakraborty
ArXiv (abs)PDFHTML

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

14 / 14 papers shown
Title
Transformers for Modeling Physical Systems
Transformers for Modeling Physical Systems
N. Geneva
N. Zabaras
AI4CE
60
143
0
04 Oct 2020
Deep Neural Networks for Nonlinear Model Order Reduction of Unsteady
  Flows
Deep Neural Networks for Nonlinear Model Order Reduction of Unsteady Flows
Hamidreza Eivazi
H. Veisi
M. H. Naderi
V. Esfahanian
AI4CE
63
173
0
02 Jul 2020
Multi-fidelity Generative Deep Learning Turbulent Flows
Multi-fidelity Generative Deep Learning Turbulent Flows
N. Geneva
N. Zabaras
AI4CE
61
44
0
08 Jun 2020
Learning functionals via LSTM neural networks for predicting vessel
  dynamics in extreme sea states
Learning functionals via LSTM neural networks for predicting vessel dynamics in extreme sea states
J. Ferrandis
M. Triantafyllou
C. Chryssostomidis
George Karniadakis
51
62
0
23 Dec 2019
Variational Autoencoders and Nonlinear ICA: A Unifying Framework
Variational Autoencoders and Nonlinear ICA: A Unifying Framework
Ilyes Khemakhem
Diederik P. Kingma
Ricardo Pio Monti
Aapo Hyvarinen
OOD
71
595
0
10 Jul 2019
Stacked Capsule Autoencoders
Stacked Capsule Autoencoders
Adam R. Kosiorek
S. Sabour
Yee Whye Teh
Geoffrey E. Hinton
OCL
48
262
0
17 Jun 2019
A Gaussian process latent force model for joint input-state estimation
  in linear structural systems
A Gaussian process latent force model for joint input-state estimation in linear structural systems
R. Nayek
S. Chakraborty
S. Narasimhan
25
91
0
29 Mar 2019
Shallow Neural Networks for Fluid Flow Reconstruction with Limited
  Sensors
Shallow Neural Networks for Fluid Flow Reconstruction with Limited Sensors
N. Benjamin Erichson
L. Mathelin
Z. Yao
Steven L. Brunton
Michael W. Mahoney
J. Nathan Kutz
AI4CE
54
34
0
20 Feb 2019
Theoretical Analysis of Auto Rate-Tuning by Batch Normalization
Theoretical Analysis of Auto Rate-Tuning by Batch Normalization
Sanjeev Arora
Zhiyuan Li
Kaifeng Lyu
80
131
0
10 Dec 2018
Quantifying model form uncertainty in Reynolds-averaged turbulence
  models with Bayesian deep neural networks
Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks
N. Geneva
N. Zabaras
BDL
49
125
0
08 Jul 2018
Linearly-Recurrent Autoencoder Networks for Learning Dynamics
Linearly-Recurrent Autoencoder Networks for Learning Dynamics
Samuel E. Otto
C. Rowley
AI4CE
46
326
0
04 Dec 2017
A Critical Review of Recurrent Neural Networks for Sequence Learning
A Critical Review of Recurrent Neural Networks for Sequence Learning
Zachary Chase Lipton
John Berkowitz
Charles Elkan
89
2,345
0
29 May 2015
Batch Normalization: Accelerating Deep Network Training by Reducing
  Internal Covariate Shift
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe
Christian Szegedy
OOD
463
43,305
0
11 Feb 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
1.9K
150,115
0
22 Dec 2014
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