Giovanni Lapenta (KU Leuven, Belgium)
- To provide efficient management and administration of the project, while fulfilling all legal requirements stated in EC rule and regulations and in the Consortium Agreement.
- To establish and maintain a functional management structure to ensure efficient communication between the partners.
- To provide assistance and advice to the partners regarding administration and reporting.
- Intellectual Property (IP) management.
- Risk management and contingency planning.
- To identify potential problems at an early stage and provide timely and effective solutions.
- To develop and implement a review and assessment structure to monitor project results with respect to objectives.
WP2: AIDApy: AI software framework, performance and new algorithms
Jorge Amaya (KU Leuven, Belgium)
- Evaluate the available AI software frameworks on multiple architectures and select the most robust option.
- Improve the computing performances of the selected software framework.
- Analyze the underlying mathematical properties of the AI algorithms implemented.
- Explore new modern algorithms for the training and deployment of AI algorithms for the analysis of Big Datasets.
WP3: AIDApy Machine Learning
Enrico Camporeale (NWO -I, Netherlands)
- To develop an high-level python front-end software that interfaces to open-source machine learning libraries. The front-end will have several degrees of verbosity, allowing for both a non-expert and an expert exploitation of the machine learning libraries.
- To analyze satellite data with unsupervised machine learning algorithms for the following purposes: discovering latent variables through (nonlinear) dimensionality reduction; classifying “lookalike” events via clustering algorithms; infer causality relationship between (latent) variables through information theoretical tools.
- To train machine learning algorithms for supervised classification based on labeled data (either from real and synthetic data), that would be able to identify certain kind of space weather events.
- To use the classification algorithm to create a catalog of interesting events.
WP4: AIDApy Statistics toolbox
Sergio Servidio (University of Calabria, Italy)
- Develop an open source code for the statistical analysis of variables, dedicated to in situ space missions.
- Unify analysis methods developed by individual researcher, providing a complete suite of data analysis tools for heliospheric experts. This goal will be achieved interconnecting the algorithms within the python language, starting from FORTRAN and C++ libraries.
- Collect data from the AIDAdb, interacting with all the work-packages.
- Extract important information about turbulence, extreme/rare phenomena and hazardous events in the heliosphere, developing also new analysis tools for the AIDApy.
- Increase performances via parallel computing.
- Develop a user-friendly platform and a GUI wizard.
Activity outcome: A PhD Thesis at UNICAL resulting from WP collaborations
WP5: AIDApy Virtual Instruments
Francesco Valentini (University of Calabria, Italy)
- Develop python softwares able to provide measurements of fields and particles by means of virtual spacecraft flying through the output data of multi-dimensional simulations of any type (single as well as multi spacecraft configuration will be implemented), collected in AIDAdb.
- Develop interpolation softwares to design virtual spacecraft trajectories within multi-dimensional simulation box; for the output of global simulations, real spacecraft trajectories will be implemented.
- Develop softwares to feed with 3D particle Velocity Distributions (VDs) from numerical simulations, in order to simulate the response of a real particle instrument (virtual top-hat).
- Develop techniques to estimate the local enhancement of plasma collisionality along a virtual spacecraft time series, due to generation of non-thermal features in the particle VDs.
- Comparatively analyze the output of the simulations and the measurements from virtual and real spacecraft (from AIDAdb), in order to support the design and training phase of novel techniques of artificial intelligence (AI) for data analysis and interpretation (AIDApy), aiming at identifying regions of scientific interest, based on time series of fields and particle VDs.
WP6: AIDApy Data Assimilation
Maria Elena Innocenti (KU Leuven, Belgium and JPL)
- Enhance an existing code for global magnetospheric simulations, OpenGGCM, with Data Assimilation techniques to improve its adherence to reality.
- Produce DA-enhanced simulations of specific events selected in collaboration with WP8.
- Feed simulation results into WP7 for inclusion into the Low Level and High Level Data Base, where they will be used for training of ML techniques and signature recognition.
WP7: Space simulations vis-a-vis in-situ observations
Francesco Califano (Physics Department University of Pisa, Italy) and Fouad Sahraoui (CNRS-Ecole Polytechnique, France)
- Define and maintain the Data Management Plan (DMP) and AIDAdb.
- Select numerical codes and simulations to use to produce data for ML training.
- Compare event lists generated by other WP with automatic and non automatic techniques.
- Give user feedback on AIDApy.
Activity outcome: A PhD Thesis at UNIPI resulting from WP collaborations
WP8: AIDApy and AIDAdb interface with external databases
Alessandro Retinò (Laboratory of Plasma Physics - CNRS, France)
- Develop a python tool to automatically select and download data from a variety of open-access in situ, remote and ground plasma data archives.
- Develop a python tool to process data and create lists of events by using routines based on human experience-driven data selection for different physical processes of interest (magnetic reconnection, turbulence, particle acceleration, etc.).
- Integrate both tools into an open-access python software and interface this tool with AIDA databases and external databases
WP9: Integration, verification and validation of the AIDA machine learning approach
Christos Theoharatos (IRIDA Labs, Greece)
- To optimize, package and deliver the ML software engine developed within AIDA project.
- To verify and validate the functionalities of the AIDApy.
- To support the overall AIDA machine learning engine.
WP10: Communications, Dissemination and Exploitation
Francesca Delli Ponti (CINECA, Italy)
- Communication of the AIDA activities and results to target audiences following the communications plan.
- Ensure the exploitation of the AIDA outcomes following the exploitation plan.
- Dissemination of the results of the project among othe EC work programs and stakeholders of the project to create awareness and incite standarisation of AI efforts.
- Promoting the scientific results of the AIDA project in the social media, traditional media and specialized newsletters.