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Activity Number: 347 - Machine Learning and Applications in Complex Engineering Systems
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract #329972 Presentation
Title: Data-Driven Modeling and Forecast of Noisy Nonlinear Dynamics
Author(s): Kyongmin Yeo* and Youngdeok Hwang and Eun Kyung Lee
Companies: IBM T.J. Watson Research Center and Sungkyunkwan University and IBM T.J. Watson Research Center
Keywords: time series; nonlinear dynamical system; stochastic process; recurrent neural network; uncertainty quntification

Data-driven modeling of a complex physical process is of current interest due to its direct relevance to cognitive manufacturing. Although domain knowledge on the underlying physical process has been playing a key role in understanding manufacturing processes, it is often impractical or impossible to build physics models from the first principles. Here, we propose a Recurrent Neural Network (RNN) based model for the nonlinear system identification and forecast of a stochastic process with an underlying physical process. The proposed RNN aims to directly estimate the probability distribution of the stochastic process by using a penalized maximum log likelihood method. It is shown that the RNN is essentially a state-space model, in which the underlying process is modeled by a set of linear dynamical systems. Also presented is a Monte Carlo procedure for a multiple-step prediction.

Authors who are presenting talks have a * after their name.

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