| Comparison of deep learning techniques to model connections between solar wind and ground magnetic perturbations AM Keesee, V Pinto, M Coughlan, C Lennox, MS Mahmud, HK Connor Frontiers in Astronomy and Space Sciences 7, 550874, 2020 | 32 | 2020 |
| Revisiting the ground magnetic field perturbations challenge: A machine learning perspective VA Pinto, AM Keesee, M Coughlan, R Mukundan, JW Johnson, ... Frontiers in Astronomy and Space Sciences 9, 869740, 2022 | 18 | 2022 |
| Probabilistic Forecasting of Ground Magnetic Perturbation Spikes at Mid‐Latitude Stations M Coughlan, A Keesee, V Pinto, R Mukundan, JP Marchezi, J Johnson, ... Space Weather 21 (6), e2023SW003446, 2023 | 1 | 2023 |
| Forecasting 1-hour Ahead Ground Magnetic Field Maximum Perturbations With Deep Learning Models VA Pinto, AM Keesee, M Coughlan, R Mukundan, JP Marchezi, ... AGU Fall Meeting Abstracts 2023 (621), NG13B-0621, 2023 | | 2023 |
| Analyzing the Influence of Magnetotail Phenomena on the Localization of Ground Magnetic Field Perturbations Using Machine Learning Interpretability Techniques M Coughlan, AM Keesee, VA Pinto, JP Marchezi, R Mukundan, ... AGU Fall Meeting Abstracts 2023, NG11A-07, 2023 | | 2023 |
| Adapting the Crossformer to Forecast Geomagnetically Induced Currents JW Johnson, F Siddiqui, M Coughlan, AM Keesee, HKIM Connor AGU Fall Meeting Abstracts 2023, NG12A-08, 2023 | | 2023 |
| On the effects of the solar wind structures in the global distribution of ground-based geomagnetic perturbations during geomagnetic storms JP Marchezi, AM Keesee, M Coughlan, R Mukundan, VA Pinto, ... AGU Fall Meeting Abstracts 2023 (2972), SM33D-2972, 2023 | | 2023 |
| Characterizing the Spatial Scales of Localized Ground-Level Magnetic Perturbations R Mukundan, AM Keesee, JP Marchezi, M Coughlan, DL Hampton, ... AGU Fall Meeting Abstracts 2023 (2967), SM33D-2967, 2023 | | 2023 |
| Investigating the Influence of Inner Magnetosphere Data on a Regional Geomagnetically Induced Current Forecasting Model R Mukundan, AM Keesee, VA Pinto, M Coughlan, H Connor AGU Fall Meeting Abstracts 2022, SM32C-1738, 2022 | | 2022 |
| Forecasting Ground Magnetic Perturbations at High and Mid-Latitudes Using Deep Learning and Near Real-Time Solar Wind Data VA Pinto, AM Keesee, M Coughlan, R Mukundan, JW Johnson, ... AGU Fall Meeting Abstracts 2022, NG52A-0153, 2022 | | 2022 |
| Forecasting of Extreme Ground Magnetic Field Fluctuations at Mid-Latitudes using Machine Learning M Coughlan, AM Keesee, VA Pinto, R Mukundan, JW Johnson, H Connor AGU Fall Meeting Abstracts 2022, SM32C-1736, 2022 | | 2022 |
| Using a Convolutional Neural Network with Uncertainty to Forecast GIC Risk of Occurrence at Mid-Latitudes MK Coughlan Proceedings of the 2nd Machine Learning in Heliophysics, 25, 2022 | | 2022 |
| Developing near real-time ground magnetic field perturbations predictions with machine learning models VA Pinto, AM Keesee, M Coughlan, R Mukundan, J Johnson, HK Connor Proceedings of the 2nd Machine Learning in Heliophysics, 26, 2022 | | 2022 |
| Evaluating Near-Real-Time Ground Magnetic Field Perturbations Predictions Using Machine Learning Models V Pinto, A Keesee, M Coughlan, R Mukundan, J Johnson, HK Connor 102nd American Meteorological Society Annual Meeting, 2022 | | 2022 |
| Establishing a benchmark for ground magnetic field perturbations predictions using machine learning models V Pinto, A Keesee, M Coughlan, R Mukundan, B Ferdousi, D Ozturk, ... AGU Fall Meeting Abstracts 2021, SA12A-05, 2021 | | 2021 |
| Using Convolutional Neural Networks and Long-Short Term Machine Learning Models to Provide Insights into GIC Drivers and Risk of Occurrence. M Coughlan, A Keesee, V Pinto, R Mukundan, H Connor, J Johnson AGU Fall Meeting Abstracts 2021, SM35B-1974, 2021 | | 2021 |
| Forecasting Ground-Level Magnetic Perturbations Using a Spherical Elementary Current System R Mukundan, A Keesee, V Pinto, M Coughlan, H Connor AGU Fall Meeting Abstracts 2021, SM41A-03, 2021 | | 2021 |
| Using Deep Learning to Forecast Ground Magnetic Field Perturbations at Midlatitudes Considering MLT Dependence V Pinto, A Keesee, M Coughlan, M Gadbois, J Johnson, HK Connor 101st American Meteorological Society Annual Meeting, 2021 | | 2021 |
| Using Machine Learning and Geomagnetic Storm Data to Determine the Risk of GIC Occurrence M Coughlan, AM Keesee, VA Pinto, JW Johnson, HK Connor AGU Fall Meeting Abstracts 2020, SM011-13, 2020 | | 2020 |
| A Deep Learning Approach to the Forecasting of Ground Magnetic Field Perturbations at High and Mid-Latitudes VA Pinto, AM Keesee, M Coughlan, MA Gadbois, JW Johnson, ... AGU Fall Meeting Abstracts 2020, NG006-05, 2020 | | 2020 |
