Project description
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.
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 (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences computational science multiphysics
- natural sciences computer and information sciences artificial intelligence machine learning
- social sciences educational sciences pedagogy active learning
- natural sciences physical sciences astronomy stellar astronomy
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-MSCA-IF-2019
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
35122 PADOVA
Italy
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.