Deep learning algorithm for data-driven simulation of noisy dynamical system
Kyongmin Yeo,Igor Melnyk +1 more
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TLDR
The deep learning model DE-LSTM, which aims to approximate the probability density function of a stochastic process via numerical discretization and the underlying nonlinear dynamics is modeled by the Long Short-Term Memory network, makes a good prediction of the probability distribution without assuming any distributional properties of the stochastics process.About:
This article is published in Journal of Computational Physics.The article was published on 2019-01-01 and is currently open access. It has received 94 citations till now. The article focuses on the topics: Probability distribution & Stochastic process.read more
Citations
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Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Posted Content
Forecasting, Structural Time Series Models and the Kalman Filter
TL;DR: In this paper, the authors provide a unified and comprehensive theory of structural time series models, including a detailed treatment of the Kalman filter for modeling economic and social time series, and address the special problems which the treatment of such series poses.
The Ensemble Kalman Filter: Theoretical formulation and practical implementation
TL;DR: The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it as mentioned in this paper, and also presents new ideas and alternative interpretations which further explain the success of the EnkF.
Journal ArticleDOI
A deep learning enabler for nonintrusive reduced order modeling of fluid flows
TL;DR: In this paper, a modular deep neural network (DNN) framework for data-driven reduced order modeling of dynamical systems relevant to fluid flows is proposed. But it is not suitable for modeling complex fluid flows.
References
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Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
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Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Book
Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
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