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

Signal and data processing for machine olfaction and chemical sensing: A review

Signal and data processing are essential elements in electronic noses as well as in most chemical sensing instruments. The multivariate responses obtained by chemical sensor arrays require signal and data processing to carry out the fundamental tasks of odor identification (classification), concentration estimation (regression), and grouping of similar odors (clustering). In the last decade, important advances have shown that proper processing can improve the robustness of the instruments against diverse perturbations, namely, environmental variables, background changes, drift, etc. This article reviews the advances made in recent years in signal and data processing for machine olfaction and chemical sensing

Intelligent flight control systems

The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms

Identification and control of dynamic systems using neural networks.

The aim of this thesis is to contribute in solving problems related to the on-line identification and control of unknown dynamic systems using feedforward neural networks. In this sense, this thesis presents new on-line learning algorithms for feedforward neural networks based upon the theory of variable structure system design, along with mathematical proofs regarding the convergence of solutions given by the algorithms; the boundedness of these solutions; and robustness features of the algorithms with respect to external perturbations affecting the neural networks' signals. In the thesis, the problems of on-line identification of the forward transfer operator, and the inverse transfer operator of unknown dynamic systems are also analysed, and neural networks-based identification schemes are proposed. These identification schemes are tested by computer simulations on linear and nonlinear unknown plants using both continuous-time and discrete-time versions of the proposed learning algorithms. The thesis reports about the direct inverse dynamics control problems using neural networks, and contributes towards solving these problems by proposing a direct inverse dynamics neural network-based control scheme with on-line learning capabilities of the inverse dynamics of the plant, and the addition of a feedback path that enables the resulting control scheme to exhibit robustness characteristics with respect to external disturbances affecting the output of the system. Computer simulation results on the performance of the mentioned control scheme in controlling linear and nonlinear plants are also included. The thesis also formulates a neural network-based internal model control scheme with on-line estimation capabilities of the forward transfer operator and the inverse transfer operator of unknown dynamic systems. The performance of this internal model control scheme is tested by computer simulations using a stable open-loop unknown plant with output signal corrupted by white noise. Finally, the thesis proposes a neural network-based adaptive control scheme where identification and control are simultaneously carried out

ADAPTIVE CONTROL BASED ON THE APPLICATION OF A SIMPLIFIED UNIFORM STRUCTURES AND LEARNING PROCEDURES

The present state of creating a new branch of Soft Computing (SC) for particular problem classes, possibly wider than the control of mechanical systems, is reported in this article. Like "traditional" SC il evades the development of analytical system models, and uses uniform structures, but these structures originate from various Lie groups. The advantages are a drastic reduction in size and an increase in lucidity. The generally "stochastic or semistochastic" "learning" or parameter tuning seems to be replaceable by simple explicit algebraic procedures of limited steps, too. The idea originated from mechanical systems\u27 control while considering their general internal symmetry group, and later it was further developed by using specific general features of it on a much wider scale. Convergence considerations are given for MIMO and SISO systems, too. Simulation examples are presented for the control of the inverted pendulum with the use of the Generalized Lorentzian Matrices. It is concluded that the me/hod is promising and probably imposes acceptable convergence requirements in many cases

An Unsupervised Neural Network for Real-Time Low-Level Control of a Mobile Robot: Noise Resistance, Stability, and Hardware Implementation

We have recently introduced a neural network mobile robot controller (NETMORC). The controller is based on earlier neural network models of biological sensory-motor control. We have shown that NETMORC is able to guide a differential drive mobile robot to an arbitrary stationary or moving target while compensating for noise and other forms of disturbance, such as wheel slippage or changes in the robot's plant. Furthermore, NETMORC is able to adapt in response to long-term changes in the robot's plant, such as a change in the radius of the wheels. In this article we first review the NETMORC architecture, and then we prove that NETMORC is asymptotically stable. After presenting a series of simulations results showing robustness to disturbances, we compare NETMORC performance on a trajectory-following task with the performance of an alternative controller. Finally, we describe preliminary results on the hardware implementation of NETMORC with the mobile robot ROBUTER.Sloan Fellowship (BR-3122), Air Force Office of Scientific Research (F49620-92-J-0499

The recursive neural network

This paper describes a special type of dynamic neural network called the Recursive Neural Network (RNN). The RNN is a single-input single-output nonlinear dynamical system with three subnets, a nonrecursive subnet and two recursive subnets. The nonrecursive subnet feeds current and previous input samples through a multi-layer perceptron with second order input units (SOMLP) [9]. In a similar fashion the two recursive subnets feed back previous output signals through SOMLPs. The outputs of the three subnets are summed to form the overall network output. The purpose of this paper is to describe the architecture of the RNN, to derive a learning algorithm for the network based on a gradient search, and to provide some examples of its use. The work in this paper is an extension of previous work on the RNN [10]. In previous work the RNN contained only two subnets, a nonrecursive subnet and a recursive subnet. Here we have added a second recursive subnet. In addition, both of the subnets in the previous RNN had linear input units. Here all three of the subnets have second order input units. In many cases this allows the RNN to solve problems more efficiently, that is with a smaller overall network. In addition, the use of the RNN for inverse modeling and control was never fully developed in the past. Here, for the first time, we derive the complete learning algorithm for the case where the RNN is used in the general model following configuration. This configuration includes the following as special cases: system modeling, nonlinear filtering, inverse modeling, nonlinear prediction and control

Neural modelling, control and optimisation of an industrial grinding process

This paper describes the development of neural model-based control strategies for the optimisation of an industrial aluminium substrate disk grinding process. The grindstone removal rate varies considerably over a stone life and is a highly nonlinear function of process variables. Using historical grindstone performance data, a NARX-based neural network model is developed. This model is then used to implement a direct inverse controller and an internal model controller based on the process settings and previous removal rates. Preliminary plant investigations show that thickness defects can be reduced by 50% or more, compared to other schemes employed