Project description DEENESFRITPL Improved simulations for stargazing Our interest in astronomy dates back to ancient times. Boosted with developments and improvements as well as the use of more advanced equipment and in-depth knowledge of the sciences, astronomy remains popular to this day. Today, astronomical research greatly depends on simulations. Unfortunately, there is no way to practically measure the realism factor in simulation, and numerical simulations tend to be too slow and expensive for prototyping new techniques or improving statistical significance. The EU-funded RISING project will address these issues by developing a framework comprising machine learning tools for a number of uses, which will find instantaneous application on dynamic simulations of star clusters and hydrodynamical simulations of their parent clouds. Show the project objective Hide the project objective Objective Contemporary astronomical research relies heavily on simulations. However, the current state of the art has no objective way to measure how `realistic’ a simulation is, nor how informative it is with respect to the scientific questions it was designed to address. Comparison between simulation and observation is left to the subjective judgment of the individual researcher. The set up of simulation sets, the choice of parameters and ingredients to include, and the number of runs to execute are all also left to the researcher’s preferences, given hardware constraints. Numerical astronomy has, as of now, no shared standard of experiment design. Additionally, numerical simulations are often so slow and expensive that it is impossible to quickly and cheaply produce new outputs to improve statistical significance or for rapid prototyping new techniques. To address these issues, I will develop the RISING framework. RISING (Realistic and Informative Simulations with machine learnING) is a bundle of machine learning tools: anomaly detection tools to measure the realism of simulations, active learning tools to plan optimal sets of simulations under resource constraints, and generative modeling tools to obtain credible simulation outputs without running the underlying simulation. RISING will find immediate application on dynamical simulations of star clusters and hydrodynamical simulations of their parent clouds, which are being run in large numbers by the ERC-funded DEMOBLACK group led by my host, Prof. Michela Mapelli. RISING will be written in Python 3.7 using the Keras API on top of Tensorflow, integrated with frameworks for multi-scale, multi-physics simulations, such as AMUSE , whose author is Prof. Portegies-Zwart (Leiden Univ.) with which Prof. Mapelli has a current ongoing collaboration. The source code of RISING and selected data products will be made freely available to the numerical astronomy community. Fields of science natural sciencescomputer and information sciencescomputational sciencemultiphysicsnatural sciencescomputer and information sciencesartificial intelligencemachine learningsocial scienceseducational sciencespedagogyactive learningnatural sciencesphysical sciencesastronomystellar astronomy Keywords Anomaly Detection Generative neural models (Variational Autoencoders Generative Adversarial Networks) Numerical Experiments Experiment Design Active Learning Programme(s) H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility Topic(s) MSCA-IF-2019 - Individual Fellowships Call for proposal H2020-MSCA-IF-2019 See other projects for this call Funding Scheme MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF) Coordinator UNIVERSITA DEGLI STUDI DI PADOVA Net EU contribution € 255 768,00 Address Via 8 febbraio 2 35122 Padova Italy See on map Region Nord-Est Veneto Padova Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00 Partners (1) Sort alphabetically Sort by Net EU contribution Expand all Collapse all Partner Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement. UNIVERSITE DE MONTREAL Canada Net EU contribution € 0,00 Address Cp 6128 station centre ville H3C3J7 Montreal See on map Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 164 031,36
