3,930 research outputs found
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented
volumes of data from field measurements, experiments and large-scale
simulations at multiple spatiotemporal scales. Machine learning offers a wealth
of techniques to extract information from data that could be translated into
knowledge about the underlying fluid mechanics. Moreover, machine learning
algorithms can augment domain knowledge and automate tasks related to flow
control and optimization. This article presents an overview of past history,
current developments, and emerging opportunities of machine learning for fluid
mechanics. It outlines fundamental machine learning methodologies and discusses
their uses for understanding, modeling, optimizing, and controlling fluid
flows. The strengths and limitations of these methods are addressed from the
perspective of scientific inquiry that considers data as an inherent part of
modeling, experimentation, and simulation. Machine learning provides a powerful
information processing framework that can enrich, and possibly even transform,
current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202
Neural system identification for large populations separating "what" and "where"
Neuroscientists classify neurons into different types that perform similar
computations at different locations in the visual field. Traditional methods
for neural system identification do not capitalize on this separation of 'what'
and 'where'. Learning deep convolutional feature spaces that are shared among
many neurons provides an exciting path forward, but the architectural design
needs to account for data limitations: While new experimental techniques enable
recordings from thousands of neurons, experimental time is limited so that one
can sample only a small fraction of each neuron's response space. Here, we show
that a major bottleneck for fitting convolutional neural networks (CNNs) to
neural data is the estimation of the individual receptive field locations, a
problem that has been scratched only at the surface thus far. We propose a CNN
architecture with a sparse readout layer factorizing the spatial (where) and
feature (what) dimensions. Our network scales well to thousands of neurons and
short recordings and can be trained end-to-end. We evaluate this architecture
on ground-truth data to explore the challenges and limitations of CNN-based
system identification. Moreover, we show that our network model outperforms
current state-of-the art system identification models of mouse primary visual
cortex.Comment: NIPS 201
A new class of multiscale lattice cell (MLC) models for spatio-temporal evolutionary image representation
Spatio-temporal evolutionary (STE) images are a class of complex dynamical systems that evolve over both space and time. With increased interest in the investigation of nonlinear complex phenomena, especially spatio-temporal behaviour governed by evolutionary laws that are dependent
on both spatial and temporal dimensions, there has been an increased need to investigate model identification methods for this class of complex systems. Compared with pure temporal processes, the identification of spatio-temporal models from observed images is much more difficult and quite
challenging. Starting with an assumption that there is no apriori information about the true model but
only observed data are available, this study introduces a new class of multiscale lattice cell (MLC)
models to represent the rules of the associated spatio-temporal evolutionary system. An application to a chemical reaction exhibiting a spatio-temporal evolutionary behaviour, is investigated to demonstrate the new modelling framework
Estimation of the mixing kernel and the disturbance covariance in IDE-based spatiotemporal systems
The integro-difference equation (IDE) is an increasingly popular mathematical model of spatiotemporal processes, such as brain dynamics, weather systems, and disease spread. We present an efficient approach for system identification based on correlation techniques for linear temporal systems that extended to spatiotemporal IDE-based models. The method is derived from the average (over time) spatial correlations of observations to calculate closed-form estimates of the spatial mixing kernel and the disturbance covariance function. Synthetic data are used to demonstrate the performance of the estimation algorithm
Lattice dynamical wavelet neural networks implemented using particle swarm optimisation for spatio-temporal system identification
Starting from the basic concept of coupled map lattices, a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNN), is introduced for spatiotemporal system identification, by combining an efficient wavelet representation with a coupled map lattice model. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimisation (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the orthogonal projection pursuit algorithm, significant wavelet-neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated waveletneurons are optimised using a particle swarm optimiser. The resultant network model, obtained in the first stage, may however be redundant. In the second stage, an orthogonal least squares (OLS) algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet-neurons from the network. The proposed two-stage hybrid training procedure can generally produce a parsimonious network model, where a ranked list of wavelet-neurons, according to the capability of each neuron to represent the total variance in the system output signal is produced. Two spatio-temporal system identification examples are presented to demonstrate the performance of the proposed new modelling framework
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