Výsledky vyhledávání - "Mandar Chandorkar"

Multiple-Hour-Ahead Forecast of the Dst Index Using a Combination of Long Short-Term Memory Neural Network and Gaussian Process

Autor: Mandar Chandorkar, Angélica Sicard, Marina A. Gruet, Enrico Camporeale

Publikováno v: Space Weather: The International Journal of Research and Applications
Space Weather: The International Journal of Research and Applications, American Geophysical Union (AGU), 2018, 16 (11), pp.1882-1896. ⟨10.1029/2018sw001898⟩
Space Weather, 16(11), 1882-1896

International audience; In this study, we present a method that combines a Long Short-Term Memory (LSTM) recurrent neural network with a Gaussian Process (GP) model to provide up to 6-hour ahead probabilistic forecasts of the Dst geomagnetic index. T

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::70a553ba633e9b48c0584cc4cc61f9b8
https://doi.org/10.1029/2018sw001898

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Probabilistic forecasting of the disturbance storm time index: An autoregressive Gaussian process approach

Autor: Simon Wing, Enrico Camporeale, Mandar Chandorkar

Publikováno v: Space Weather, 15(8), 1004-1019

We present a methodology for generating probabilistic predictions for the Disturbance Storm Time(Dst) geomagnetic activity index. We focus on the One Step Ahead prediction task and use the OMNI hourly resolution data to build our models. Our proposed

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ef7edbfb6a53577669fcc720d39d8c60
https://doi.org/10.1002/2017sw001627

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On the propagation of uncertainties in radiation belt simulations

Autor: Simon Wing, Enrico Camporeale, Mandar Chandorkar, Alexander Drozdov, Yuri Shprits

Publikováno v: Space Weather, 14(11), 982-992
Space Weather

We present the first study of the uncertainties associated with radiation belt simulations, performed in the standard quasi-linear diffusion framework. In particular, we estimate how uncertainties of some input parameters propagate through the nonlin

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::edb3ed070d643c994ef03bafbdd3c187
https://doi.org/10.1002/2016sw001494

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Contributors

Autor: Livia R. Alves, Vassilis Angelopoulos, Daniel N. Baker, Ramkumar Bala, Michael Balikhin, Jacob Bortnik, Richard Boynton, Enrico Camporeale, Algo Carè, Mandar Chandorkar, Xiangning Chu, Giuseppe Consolini, FLARECAST Consortium, Véronique Delouille, Richard E. Denton, Mike Hapgood, Verena Heidrich-Meisner, Stefan J. Hofmeister, George B. Hospodarsky, Paulo R. Jauer, Jay R. Johnson, Shrikanth G. Kanekal, Adam Kellerman, Craig A. Kletzing, Daiki Koga, Alisson Dal Lago, Zi-Qiang Lang, Wen Li, Marco Loog, Qianli Ma, Benjamin Mampaey, Anna M. Massone, Claudia Medeiros, Michele Piana, Geoffrey D. Reeves, Patricia Reiff, Martin A. Reiss, Yuri Y. Shprits, Ligia A. Da Silva, Vitor M. Souza, Harlan E. Spence, Maria Spasojevic, Manuela Temmer, Richard M. Thorne, Astrid Veronig, Luis E.A. Vieira, Hua-Liang Wei, Robert F. Wimmer-Schweingruber, Simon Wing, Xiaojia Zhang, Irina S. Zhelavskaya

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d22a55cc29aa44d11cd7640b6c8afec3
https://doi.org/10.1016/b978-0-12-811788-0.09990-x

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Probabilistic Forecasting of Geomagnetic Indices Using Gaussian Process Models

Autor: Mandar Chandorkar, Enrico Camporeale

In this chapter, we give the reader an in-depth view into building of probabilistic forecasting models for geomagnetic time series using the Gaussian process methodology outlined in the previous chapters. We highlight design decisions and practical i

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::91e3d16dd1937b4d35303f27d42a286a
https://doi.org/10.1016/b978-0-12-811788-0.00009-3

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Fixed-Size Least Squares Support Vector Machines: Scala Implementation for Large Scale Classification

Autor: Mandar Chandorkar, Raghvendra Mall, Bart De Moor, Oliver Lauwers, Johan A. K. Suykens

Publikováno v: SSCI

We propose FS-Scala, a flexible and modular Scala based implementation of the Fixed Size Least Squares Support Vector Machine (FS-LSSVM) for large data sets. The framework consists of a set of modules for (gradient and gradient free) optimization, mo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9b2566b910f818f15a686db3323c1fd9
https://doi.org/10.1109/ssci.2015.83

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Akademický článek

Identification of Radial Diffusion Parameters for the Earth's Radiation Belt through Bayesian Inference.

Autor: Sarma, Rakesh¹ (AUTHOR) rakesh@cwi.nl, Chandorkar, Mandar¹ (AUTHOR), Camporeale, Enrico^1,2 (AUTHOR), Drozdov, Alexander³ (AUTHOR), Shprits, Yuri⁴ (AUTHOR)

Publikováno v: Geophysical Research Abstracts. 2019, Vol. 21, p1-1. 1p.

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Akademický článek

Bayesian Inference of Quasi‐Linear Radial Diffusion Parameters using Van Allen Probes.

Autor: Sarma, Rakesh, Chandorkar, Mandar, Zhelavskaya, Irina, Shprits, Yuri, Drozdov, Alexander, Camporeale, Enrico

Publikováno v: Journal of Geophysical Research. Space Physics; May2020, Vol. 125 Issue 5, p1-15, 15p

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Conference

Fixed-Size Least Squares Support Vector Machines: Scala Implementation for Large Scale Classification.

Autor: Chandorkar, Mandar, Mall, Raghvendra, Lauwers, Oliver, Suykens, Johan A.K., Moor, Bart De

Publikováno v: 2015 IEEE Symposium Series on Computational Intelligence; 2015, p522-528, 7p

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Conference

Table of Contents.

Publikováno v: 2015 IEEE Symposium Series on Computational Intelligence; 2015, pv-xxvii, 23p

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