| Multiple‐hour‐ahead forecast of the Dst index using a combination of long short‐term memory neural network and Gaussian process MA Gruet, M Chandorkar, A Sicard, E Camporeale Space Weather 16 (11), 1882-1896, 2018 | 87 | 2018 |
| Probabilistic forecasting of the disturbance storm time index: An autoregressive Gaussian process approach M Chandorkar, E Camporeale, S Wing Space Weather 15 (8), 1004-1019, 2017 | 54 | 2017 |
| On the propagation of uncertainties in radiation belt simulations E Camporeale, Y Shprits, M Chandorkar, A Drozdov, S Wing Space Weather 14 (11), 982-992, 2016 | 16 | 2016 |
| Bayesian inference of quasi‐linear radial diffusion parameters using Van Allen Probes R Sarma, M Chandorkar, I Zhelavskaya, Y Shprits, A Drozdov, ... Journal of Geophysical Research: Space Physics 125 (5), e2019JA027618, 2020 | 12 | 2020 |
| Dynamic time lag regression: Predicting what and when M Chandorkar, C Furtlehner, B Poduval, E Camporeale, M Sebag ICLR 2020-8th International Conference on Learning Representations, 2020 | 10 | 2020 |
| Probabilistic forecasting of geomagnetic indices using Gaussian process models M Chandorkar, E Camporeale Machine learning techniques for space weather, 237-258, 2018 | 9 | 2018 |
| Fixed-size least squares support vector machines: Scala implementation for large scale classification M Chandorkar, R Mall, O Lauwers, JAK Suykens, B De Moor 2015 IEEE Symposium Series on Computational Intelligence, 522-528, 2015 | 9 | 2015 |
| Machine learning in space weather M Chandorkar Université of Eindhoven, 2019 | 4 | 2019 |
| Fixed Size Least Squares Support Vector Machines: A Scala based programming framework for Large Scale Classification M Chandorkar Katholieke Universiteit Leuven, 2015 | 3 | 2015 |
| Bayesian inference of radiation belt loss timescales. E Camporeale, M Chandorkar AGU Fall Meeting Abstracts 2017, SM23A-2583, 2017 | 1 | 2017 |
| Gaussian Process Models for One Hour Ahead Prediction of the Dst Index. M Chandorkar, E Camporeale, S Wing AGU Fall Meeting Abstracts, SH11C-2261, 2016 | 1 | 2016 |
| Dynamic Time Lag Regression: Predicting Time Lagged Effects of Solar Activity M Chandorkar, E Camporeale, B Poduval, C Furthlener, M Sebag AGU Fall Meeting 2019, 2019 | | 2019 |
| Dynamic Time Lag Regression: Predicting Time Lagged Effects of Solar Activity B Poduval, M Chandorkar, E Camporeale, C Furthlener, M Sebag AGU Fall Meeting Abstracts 2019, NG22A-05, 2019 | | 2019 |
| Machine learning in space weather: forecasting, identification & uncertainty quantification MH Chandorkar | | 2019 |
| Predicting Time Lagged Effects of Solar Activity: A Deep Learning Approach M Chandorkar Proceedings of Machine Learning in Heliophysics, 29, 2019 | | 2019 |
| A Deep Learning Approach to Forecast Tomorrow's Solar Wind Parameters C Shneider Proceedings of Machine Learning in Heliophysics, 57, 2019 | | 2019 |
| A Deep Learning Approach to forecasting solar wind properties E Camporeale, C Shneider, M Chandorkar, B Poduval Chapman Conference on Scientific Challenges Pertaining to Space Weather …, 2019 | | 2019 |
| Identification of Radial Diffusion Parameters for the Earth's Radiation Belt through Bayesian Inference. R Sarma, M Chandorkar, E Camporeale, A Drozdov, Y Shprits Geophysical Research Abstracts 21, 2019 | | 2019 |
| Bayesian Inference of Radial Diffusion Parameters for the Earth's Radiation Belt: a Deep Learning Framework R Sarma, M Chandorkar, E Camporeale, A Drozdov, Y Shprits AGU Fall Meeting 2018, 2018 | | 2018 |
| Bayesian Inference of Radial Diffusion Parameters for the Earth's Radiation Belt: a Deep Learning Framework E Camporeale, R Sarma, M Chandorkar, A Drozdov, Y Shprits AGU Fall Meeting Abstracts 2018, SM31D-3515, 2018 | | 2018 |
Enrico CamporealeUniversity of Colorado, BoulderVahvistettu sähköpostiosoite verkkotunnuksessa noaa.gov
