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Sensing is a universal task in science and engineering.
On a Measure of the Information Provided by an Experiment
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Maximum Entropy Sampling and Optimal Bayesian Experimental Design
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Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation
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An Improved In Situ and Satellite SST Analysis for Climate
Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C. & Wang, W · 2002
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An ‘empirical interpolation’ method: application to efficient reduced-basis discretization of partial differential equations
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Convex Optimization (2004)
Boyd, S. & Vandenberghe, L · 2004
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Asymptotic theory of information-theoretic experimental design
Paninski, L · 2005
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Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation (2021)
Kasai, J., Pappas, N., Peng, H., Cross, J. & Smith, N. A · 2006
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Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies
Krause, A., Singh, A. & Guestrin, C · 2008
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A public turbulence database cluster and applications to study Lagrangian evolution of velocity increments in turbulence
Li, Y. et al · 2008
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Sensor Selection via Convex Optimization
Joshi, S. & Boyd, S · 2009
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Nonlinear Model Reduction via Discrete Empirical Interpolation
Chaturantabut, S. & Sorensen, D. C · 2010
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A Practical Guide to Applying Echo State Networks
Lukoševičius, M · 2012
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Analysis of Observed Chaotic Data (Springer Science & Business Media, 2012)
Abarbanel, H · 2012
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Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data (Oxford University Press, Oxford, 2013), 1st edition edn
Kutz, J. N · 2013
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Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling (2014)
Chung, J., Gulcehre, C., Cho, K. & Bengio, Y · 2014
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Deep learning
LeCun, Y., Bengio, Y. & Hinton, G · 2015
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A New Selection Operator for the Discrete Empirical Interpolation Method—Improved A Priori Error Bound and Extensions
Linking gaussian process regression with data-driven manifold embeddings for nonlinear data fusion
Lee, S., Dietrich, F., Karniadakis, G. E. & Kevrekidis, I. G · 2019
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Shallow neural networks for fluid flow reconstruction with limited sensors
Erichson, N. B. et al · 2020
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Deep reconstruction of strange attractors from time series
Gilpin, W · 2020
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Sensor Selection With Cost Constraints for Dynamically Relevant Bases
Clark, E., Kutz, J. N. & Brunton, S. L · 2020
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Time-series machine-learning error models for approximate solutions to parameterized dynamical systems
Parish, E. J. & Carlberg, K. T · 2020
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On the structure of time-delay embedding in linear models of non-linear dynamical systems
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Drmač, Z. & Gugercin, S · 2016
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S. & Sun, J · 2016
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Adam: A Method for Stochastic Optimization (2017)
Kingma, D. P. & Ba, J · 2017
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Chaos as an intermittently forced linear system
Brunton, S. L., Brunton, B. W., Proctor, J. L., Kaiser, E. & Kutz, J. N · 2017
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Ergodic theory, dynamic mode decomposition, and computation of spectral properties of the koopman operator
Arbabi, H. & Mezic, I · 2017
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Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns
Manohar, K., Brunton, B. W., Kutz, J. N. & Brunton, S. L · 2018
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Flowfield Reconstruction Method Using Artificial Neural Network
Yu, J. & Hesthaven, J. S · 2019
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Pan, S. & Duraisamy, K · 2020
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Time-delay observables for koopman: Theory and applications
Kamb, M., Kaiser, E., Brunton, S. L. & Kutz, J. N · 2020
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Data-driven sparse reconstruction of flow over a stalled aerofoil using experimental data
Carter, D. W., Voogt, F. D., Soares, R. & Ganapathisubramani, B · 2021
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Multi-Fidelity Sensor Selection: Greedy Algorithms to Place Cheap and Expensive Sensors With Cost Constraints
Clark, E., Brunton, S. L. & Kutz, J. N · 2021
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A survey on long short-term memory networks for time series prediction
Lindemann, B., Müller, T., Vietz, H., Jazdi, N. & Weyrich, M · 2021
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Dynamical time series embeddings in recurrent neural networks
Uribarri, G. & Mindlin, G. B · 2022
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Wavefront sensor fusion via shallow decoder neural networks for aero-optical predictive control
Sahba, S. et al · 2022
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Data-driven sensor placement with shallow decoder networks (2022)
Williams, J., Zahn, O. & Kutz, J. N · 2022
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Discovering governing equations from partial measurements with deep delay autoencoders
Bakarji, J., Champion, K., Kutz, J. N. & Brunton, S. L · 2022
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Deep learning delay coordinate dynamics for chaotic attractors from partial observable data
Young, C. D. & Graham, M. D · 2023
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Attention Is All You Need (2023)
Vaswani, A. et al · 2023
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Leveraging arbitrary mobile sensor trajectories with shallow recurrent decoder networks for full-state reconstruction (2023)
Ebers, M. R., Williams, J. P., Steele, K. M. & Kutz, J. N · 2023
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