{"id":387,"date":"2023-03-12T23:44:56","date_gmt":"2023-03-12T21:44:56","guid":{"rendered":"https:\/\/mosaiics.astro.bas.bg\/?page_id=387"},"modified":"2023-03-20T12:04:42","modified_gmt":"2023-03-20T10:04:42","slug":"machine-learning-and-computer-vision-in-heliophysics-2023","status":"publish","type":"page","link":"https:\/\/mosaiics.astro.bas.bg\/?page_id=387","title":{"rendered":""},"content":{"rendered":"\n
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INTERNATIONAL WORKSHOP ON <\/mark><\/strong>
MACHINE LEARNING AND COMPUTER VISION <\/mark><\/strong>
IN HELIOPHYSICS<\/mark><\/strong>
19-21 April, 2023<\/mark><\/strong>
Sofia, Bulgaria<\/mark><\/strong>
and online<\/mark><\/strong><\/h3>\n\n\n\n
\n
Back<\/a><\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n

Wednesday, April 19, 2023<\/mark><\/strong><\/h4>\n\n\n\n

8:30 \u2013 9:00 Registration and Introduction<\/strong><\/h4>\n\n\n\n

09:00 \u2013 09:40<\/strong> (Invited) <\/strong>Michele Piana \u2013 Artificial intelligence for space weather forecasting: data-driven and physics-informed approaches in research and operational settings<\/em><\/mark><\/p>\n\n\n\n

Session 1 \u2013 Machine Learning \/ Computer Vision Techniques<\/mark><\/strong><\/p>\n\n\n\n

09:40 \u2013 10:00 <\/strong>Vanessa Mercea \u2013 A Machine Learning Enhanced Approach for Automated Sunquake Detection in Acoustic Emission Maps<\/em><\/mark><\/p>\n\n\n\n

10:00 \u2013 10:20 <\/strong>Francesco Pio Ramunno \u2013 Modeling Solar Images from SDO\/AIA with Denoising Diffusion Models<\/em><\/mark><\/p>\n\n\n\n

10:20 \u2013 10:40<\/strong> Manuel Luna \u2013 Towards a technique for automatic detection and characterisation of oscillations in solar filaments<\/em><\/mark><\/p>\n\n\n\n

10:40 \u2013 11:20 Coffee Break<\/mark><\/strong><\/p>\n\n\n\n

11:20 \u2013 11:40 <\/strong>Connor O’Brien \u2013 Non-Deterministic Models of Solar Wind Propagation from L1 to the Earth<\/em><\/mark><\/p>\n\n\n\n

11:40 \u2013 12:00<\/strong> Jeremiah Scully \u2013 Mitigation of Radio Frequency Interference in Solar Radio observations using Generative Adversarial Networks<\/em><\/mark><\/p>\n\n\n\n

12:00 \u2013 12:20<\/strong> Matthew Lennard \u2013 Fast Feature Recovery for Flux Emergence Forecasting in the Photosphere using Neural Networks<\/em><\/mark><\/p>\n\n\n\n

12:20 \u2013 12:40<\/strong> Juan Esteban Agudelo Ortiz \u2013 Deep Learning techniques implementation for the generation of stokes parameters and atmospheric parameters in the solar context<\/em><\/mark><\/p>\n\n\n\n

12:40 \u2013 14:20 Lunch<\/mark><\/strong><\/p>\n\n\n\n

14:20 \u2013 14:40<\/strong> Talha Siddique \u2013 A Cluster of Machine Learning Enabled Magnetometer System For Online Training And Prediction of Geomagnetic Disturbances<\/em><\/mark><\/p>\n\n\n\n

14:40 \u2013 15:00 <\/strong>Gonzalo Cucho-Padin \u2013 Three-dimensional reconstruction of ion flux in the Earth\u2019s northern cusp based on artificial neural networks<\/em><\/mark><\/p>\n\n\n\n

15:00 \u2013 15:20 <\/strong>Daniel Carpenter \u2013 A Customized Distance Metric for Explainable In-Situ Solar Wind Clustering<\/em><\/mark><\/p>\n\n\n\n

15:20 \u2013 15:40<\/strong> Andong Hu \u2013 Multi-Hour-Ahead Geoelectric Fields Forecast Using Multi-fidelity Machine Learning Method<\/em><\/mark><\/p>\n\n\n\n

15:40 \u2013 16:20<\/strong> Coffee Break<\/strong><\/mark><\/p>\n\n\n\n

16:20 \u2013 17:00 (Invited) <\/strong>Enrico Camporeale \u2013 <\/strong>Data-Driven Discovery of Fokker-Planck Equation for the Earth’s Radiation Belts Electrons Using Physics-Informed Neural Networks<\/em><\/mark>\u00a0<\/p>\n\n\n\n

Session 2 \u2013 Machine Learning \/ Computer Vision Applications in Heliophysics<\/mark><\/strong><\/p>\n\n\n\n

17:00 \u2013 17:20 <\/strong>Spyros Kasapis \u2013 Turning Noise into Data: Characterization of the Van Allen Radiation Belt Using SDO Spikes Data<\/em><\/mark><\/p>\n\n\n\n

17:20 \u2013 17:40<\/strong> Ricardo Gafeira \u2013 PCA-NN model for TEC with space weather parameters as predictors: tuning of NN algorithms and input parameters<\/em><\/mark><\/p>\n\n\n\n

17:40 \u2013 18:00<\/strong> Benoit Tremblay \u2013 SuNeRFs: The Sun as a (fully-resolved) Star<\/em><\/mark><\/p>\n\n\n\n

18:00 \u2013 20:00 Reception <\/mark><\/strong><\/p>\n\n\n\n

Thursday April 20, 2023<\/mark><\/strong><\/h4>\n\n\n\n

Session 2 \u2013 Machine Learning \/ Computer Vision Applications in Heliophysics<\/mark><\/strong><\/p>\n\n\n\n

9:00 \u2013 9:40 (Invited) <\/strong>Athanasios Papaioannou \u2013 Predicting Solar Activity (flares, CMEs & SEPs) using Machine-Learning<\/em><\/mark><\/p>\n\n\n\n

9:40 \u2013 10:00 <\/strong>Hanne Baeke \u2013 Classification of Solar Flares using Data Analysis and Clustering of Active Regions<\/em><\/mark><\/p>\n\n\n\n

10:00 \u2013 10:20<\/strong> Hemapriya Raju \u2013 Dynamic time based eruptive flare prediction using machine learning<\/em><\/mark><\/p>\n\n\n\n

10:20 \u2013 10:40 <\/strong>Mohamed Nedal \u2013 Predicting the Solar Energetic Proton Integral Flux with Deep Learning Models<\/em><\/mark><\/p>\n\n\n\n

10:40 \u2013 11:20 Coffee Break<\/mark><\/strong><\/p>\n\n\n\n

11:20 \u2013 11:40 <\/strong>Suhaila Binti M Buhari \u2013 Equatorial Plasma Bubble Prediction Model Using Satellite Data<\/em><\/mark><\/p>\n\n\n\n

11:40 \u2013 12:00 <\/strong>Philippe Garnier \u2013 Martian bow shock detection with machine learning<\/em><\/mark><\/p>\n\n\n\n

12:00 \u2013 12:20 <\/strong>Pearse Murphy \u2013 Automatic recognition of solar radio bursts in NenuFAR observations<\/em><\/mark><\/p>\n\n\n\n

12:20 \u2013 12:40 <\/strong>Akhil Gunessee \u2013 Can a deep learning approach of detecting solar radio bursts perform better than the interquartile range threshold outlier detection method?<\/em><\/mark><\/p>\n\n\n\n

12:40 \u2013 14:20 Lunch<\/mark><\/strong><\/p>\n\n\n\n

14:20 \u2013 14:40 <\/strong>Mario Fernandez \u2013 deARCE solar burst detection system applied to unlabeled e-Callisto data<\/em><\/mark><\/p>\n\n\n\n

14:40 \u2013 15:00<\/strong> Andrea Diercke \u2013 A Universal Method for Solar Filament Detection from H-alpha Observations using Semi-supervised Deep Learning<\/em><\/mark><\/p>\n\n\n\n

15:00 \u2013 15:20<\/strong> Aparna Venkataramanasastry \u2013 Applying machine learning to find Filament Chirality<\/em><\/mark><\/p>\n\n\n\n

15:20 \u2013 15:40<\/strong> Alin Paraschiv \u2013 Predicting the Geoeffectiveness of CMEs Using Machine Learning<\/em><\/mark><\/p>\n\n\n\n

15:40 \u2013 16:00 Coffee Break<\/strong><\/p>\n\n\n\n

16:00 \u2013 18:30 Poster Session<\/strong>
James Wanliss – Forecasting Space Weather with Physics-Based Input and Temporal Convolutional Neural Networks<\/em>
Milo Buitrago-Casas – A real-time solar flare alert system for early flare physics studies: Exploratory data analysis<\/em>
Gr\u00e9goire Francisco – Insight on Flare Forecast with Explainable Deep Learning<\/em>
Christoph Schirninger – Deep learning image-burst stacking for post-processing of high-resolution ground-based solar observations<\/em>
Adeline Paiement – Removing cloud shadows from ground-based solar imagery<\/em>
Niels Sayez – Segmentation, grouping and classification of sunspots from ground-based observations using deep learning methods<\/em>
Iaroslav Gorbachev – Application of Deep Learning techniques for Stokes inversions using the Milne-Eddington approximation based on GRIS data<\/em>
Susan Palacio Salcedo – Automatic classification of Range-Time-Intensity maps of Equatorial Spread-F<\/em>
Julio Hernandez Camero – Building a Coronal Mass Ejection source region catalogue for Machine Learning based space weather forecasting<\/em>
Maike Bauer – Automated CME detection and tracking in HI<\/em>
Valeria Sieyra – Understanding CME deflections<\/em>
Saida Milena D\u00edaz Castillo – Exploring U-net + LSTM networks for classification and segmentation of evolving granular structures<\/em>
Daniel Collin – Forecasting solar wind speed from solar EUV images<\/em>
Aatiya Ali – Understanding Predictability of Solar Proton Events from GOES statistical features and MHD coronal models.<\/em>
Kamen Kozarev – Improving LOFAR Solar Radio Imaging Observations With Machine Learning<\/em><\/p>\n\n\n\n

19:00 \u2013 21:00 Conference Dinner<\/strong><\/p>\n\n\n\n

Friday April 21, 2023<\/strong><\/p>\n\n\n\n

Session 2 \u2013 Machine Learning- and Computer Vision-Based Tools<\/strong><\/p>\n\n\n\n

9:00 \u2013 9:40 (Invited) <\/strong>Robert Jarolim \u2013 Physics informed neural networks and application to solar magnetic field simulations<\/em><\/p>\n\n\n\n

9:40 \u2013 10:00 <\/strong>Senthamizh Pavai Valliappan \u2013 Performance analysis of AI generated solar farside magnetograms in EUHFORIA<\/em><\/p>\n\n\n\n

10:00 \u2013 10:20 <\/strong>Jorge Amaya \u2013 Parametrization of solar active regions using Variational Autoencoders<\/em><\/p>\n\n\n\n

10:20 \u2013 10:40<\/strong> Hannah T. R\u00fcdisser \u2013 Automatic Detection of Interplanetary Coronal Mass Ejections<\/em><\/p>\n\n\n\n

10:40 \u2013 11:20 Coffee Break<\/strong><\/p>\n\n\n\n

11:20 \u2013 11:40<\/strong> Felix Nakotey Minta \u2013 Forecasting the Transit Time of Earth-directed Halo CMEs Using Artificial Neural Network: A case study application of GCS forward-modelling technique<\/em><\/p>\n\n\n\n

11:40 \u2013 12:00<\/strong> <\/em>Harshita Gandhi \u2013 Probing the True Nature of CMEs using GCS-based Large Statistics of Multi-viewpoint Observations<\/em><\/p>\n\n\n\n

12:00 \u2013 12:20<\/strong> <\/em>Henrik Eklund \u2013 Artificial neural network based spatio-temporal deconvolver for refinement of solar images<\/em><\/p>\n\n\n\n

12:20 \u2013 12:40<\/strong> <\/em>Slava Bourgeois \u2013 Machine Learning\/Mathematical Morphology coupling for solar features detection<\/em><\/p>\n\n\n\n

12:40 \u2013 14:20 Lunch<\/strong>

Session 4 \u2013 Machine Learning- and Computer Vision-Based Tools<\/mark><\/strong><\/p>\n\n\n\n

14:20 \u2013 15:00<\/strong> (Invited)<\/strong> Alexander Engell \u2013 SPRINTS: A machine learning ecosystem for forecasting solar-driven events and scientific event crowd-sourcing<\/em><\/p>\n\n\n\n

15:00 \u2013 15:20<\/strong> Manolis Georgoulis \u2013 Benchmark Datasets for Solar Weather Forecasting Applications<\/em><\/p>\n\n\n\n

15:20 \u2013 15:40<\/strong> Oleg Stepanyuk \u2013 Advanced Multi-Instrument Image Processing and Feature Tracking for Remote CME Characterization with Convolutional Neural Network<\/em><\/p>\n\n\n\n

15:40 \u2013 16:20 Coffee Break<\/strong><\/p>\n\n\n\n

16:20 \u2013 16:40<\/strong> Mattia Mancini \u2013 Making LOFAR Data Accessible to the Solar and Space Weather Community<\/em><\/p>\n\n\n\n

16:40 \u2013 17:00 <\/strong>Shane Maloney \u2013 ARCAFF Project Early Results<\/em><\/p>\n\n\n\n

17:00 \u2013 17:20 <\/strong>Christopher Prior \u2013 ARTop: a program to calculate novel topology-based predictive metrics of active region magnetic field structure.<\/em><\/p>\n\n\n\n

17:20 \u2013 17:40 <\/strong>Simon Mackovjak \u2013 Feasibility study of data-driven Autonomous Service for Prediction of Ionospheric Scintillations (ASPIS)<\/em><\/p>\n\n\n\n

17:40 \u2013 18:00 <\/strong>Katya Verner \u2013 The Very First NASA AI\/ML Crowdsourcing Challenge Results Using SOHO\/LASCO Data<\/em>

18:00 \u2013 18:30 Discussion and Farewell<\/strong><\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

INTERNATIONAL WORKSHOP ON MACHINE LEARNING AND COMPUTER VISION IN HELIOPHYSICS19-21 April, 2023Sofia, Bulgariaand online Wednesday, April 19, 2023 8:30 \u2013 9:00 Registration and Introduction 09:00 \u2013 09:40 (Invited) Michele Piana \u2013 Artificial intelligence for space weather forecasting: data-driven and physics-informed approaches in research and operational settings Session 1 \u2013 Machine Learning \/ Computer Vision Techniques<\/p>\n

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