Assessment of End-to-End and Sequential Data-driven Learning of Fluid Flows
arXiv:1806.08234 · doi:10.1007/s10444-020-09753-7
Abstract
In this work we explore the advantages of end-to-end learning of multilayer maps offered by feed forward neural-networks (FFNN) for learning and predicting dynamics from transient fluid flow data.While machine learning in general depends on data quality and quantity relative to the underlying dynamics of the system, it is important for a given learning architecture to make the most of this available information. To this end, we focus on data-driven problems where there is a need to predict over reasonable time into the future with limited data availability. Such function approximation or time series prediction is in contrast to many applications of machine learning such as pattern recognition and parameter estimation that leverage vast datasets. In this study, we interpret the suite of recently popular data-driven learning approaches that approximate the dynamics as Markov linear model in a higher-dimensional feature space as a multilayer architecture similar to neural networks.
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References in corpus (12)
- Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
- A Data-Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition
- Deep learning for universal linear embeddings of nonlinear dynamics
- Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations
- Koopman invariant subspaces and finite linear representations of nonlinear dynamical systems for control
- PDE-Net: Learning PDEs from Data
- Long-time predictive modeling of nonlinear dynamical systems using neural networks
- A Kernel-Based Approach to Data-Driven Koopman Spectral Analysis
- Variational approach for learning Markov processes from time series data
- Higher Order Recurrent Neural Networks
- Sparse Convolution-based Markov Models for Nonlinear Fluid Flows
- Wind Estimation Using Quadcopter Motion: A Machine Learning Approach