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Neural network closures for nonlinear model order reduction
Omer San
, Romit Maulik
College of Information Sciences and Technology
Institute for Computational and Data Sciences (ICDS)
Research output
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Contribution to journal
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peer-review
131
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Dive into the research topics of 'Neural network closures for nonlinear model order reduction'. Together they form a unique fingerprint.
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Keyphrases
Neural Network
100%
Parameter Change
100%
Nonlinear Model Order Reduction
100%
Network Closure
100%
Computationally Efficient
50%
Nonlinear Systems
50%
Computation Overhead
50%
Reliability Prediction
50%
Proposed Methodology
50%
Proper Orthogonal Decomposition
50%
Fluid Dynamics
50%
Reduced Order Model
50%
Complex Dynamical Systems
50%
Non-stationary System
50%
Dynamic Applications
50%
Quadratic Nonlinearity
50%
Fourier Basis
50%
Feedforward Neural Network
50%
Nonlinear Dependence
50%
Galerkin Projection
50%
Neural Network Architecture
50%
Robust Machine Learning
50%
Machine Learning Framework
50%
Nonlinear Advection
50%
Extreme Learning Machine
50%
Engineering
Nonlinear Model
100%
Neural Network Architecture
100%
Basis Function
50%
Feedforward
50%
Proper Orthogonal Decomposition
50%
Fluid Dynamics
50%
Reduced Order Model
50%
Regularization
50%
Fourier Series
50%
Learning System
50%
Nonlinearity
50%
Advection
50%
Extreme Learning Machine
50%
Chemical Engineering
Learning System
100%
Neural Network
100%
Feedforward Neural Network
50%