Shallow neural networks for fluid flow reconstruction with limited sensors
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Publication:5160984
DOI10.1098/rspa.2020.0097zbMath1472.68172arXiv1902.07358OpenAlexW3035295279WikidataQ98649521 ScholiaQ98649521MaRDI QIDQ5160984
Lionel Mathelin, Steven L. Brunton, Zhewei Yao, Michael W. Mahoney, N. Benjamin Erichson, J. Nathan Kutz
Publication date: 29 October 2021
Published in: Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1902.07358
neural networksartificial intelligencemachine learningsensorscomputational physicsfluid dynamicsmechanical engineeringflow field estimation
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Cites Work
- Unnamed Item
- Unnamed Item
- Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
- Sparse Principal Component Analysis via Variable Projection
- A fast immersed boundary method using a nullspace approach and multi-domain far-field boundary conditions
- Observable dictionary learning for high-dimensional statistical inference
- Hidden physics models: machine learning of nonlinear partial differential equations
- An `empirical interpolation' method: Application to efficient reduced-basis discretization of partial differential equations
- Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning
- The immersed boundary method: a projection approach
- Unsteady flow sensing and estimation via the gappy proper orthogonal decomposition
- A New Selection Operator for the Discrete Empirical Interpolation Method---Improved A Priori Error Bound and Extensions
- A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems
- A public turbulence database cluster and applications to study Lagrangian evolution of velocity increments in turbulence
- Model Reduction for Flow Analysis and Control
- Nonlinear Model Reduction via Discrete Empirical Interpolation
- Randomized Algorithms for Matrices and Data
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- Image restoration using a neural network
- Acceleration of Stochastic Approximation by Averaging
- A hierarchy of low-dimensional models for the transient and post-transient cylinder wake
- Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks
- Solving ill-posed inverse problems using iterative deep neural networks
- Deep Convolutional Neural Network for Inverse Problems in Imaging
- Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems
- A Statistical View of Some Chemometrics Regression Tools
- Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns
- Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows
- Randomized Dynamic Mode Decomposition
- Reconciling modern machine-learning practice and the classical bias–variance trade-off
- Regularization and Variable Selection Via the Elastic Net
- Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
- Image Super-Resolution Via Sparse Representation
- Compressed sensing
