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