Explore the words cloud of the BLACKJACK project. It provides you a very rough idea of what is the project "BLACKJACK" about.
The following table provides information about the project.
CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
|Coordinator Country||France [FR]|
|Total cost||1˙489˙000 €|
|EC max contribution||1˙489˙000 € (100%)|
1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
|Duration (year-month-day)||from 2020-02-01 to 2025-01-31|
Take a look of project's partnership.
|1||CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS||FR (PARIS)||coordinator||1˙489˙000.00|
Expensive computer simulations have become routine in the experimental sciences. Astrophysicists design complex models of the evolution of galaxies, biologists develop intricate models of cells, ecologists model the dynamics of ecosystems at a world scale. A single evaluation of such complex models takes minutes or hours on today's hardware. On the other hand, fitting these models to data can require millions of serial evaluations. Monte Carlo methods, for example, are ubiquitous in statistical inference for scientific data, but they scale poorly with the number of model evaluations. Meanwhile, the use of parallel computing architectures for Monte Carlo is often limited to running independent copies of the same algorithm. Blackjack will provide Monte Carlo methods that unlock inference for expensive models in biology by directly addressing the slow rate of convergence and the parallelization of Monte Carlo methods.
The key to take down the Monte Carlo rate is to introduce repulsiveness between the quadrature nodes. For instance, we recently proved that determinantal point processes, a prototypal repulsive distribution introduced in physics, improve the Monte Carlo convergence rate, just like electrons lead to low-variance estimation of volumes by efficiently filling a box. Such results lead to open computational and statistical challenges. We propose to solve these challenges, and make repulsive processes a novel tool for applied statisticians, signal processers, and machine learners.
Still with repulsiveness as a hammer, we will design the first parallel Markov chain Monte Carlo algorithms that are qualitatively different from running independent copies of known algorithms, i.e., that explicitly improve the order of convergence of the single-machine algorithm. To this end, we will turn mathematical tools such as repulsive particle systems and non-colliding processes into computationally cheap, communication-efficient Monte Carlo schemes with fast convergence.
Are you the coordinator (or a participant) of this project? Plaese send me more information about the "BLACKJACK" project.
For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.
Send me an email (firstname.lastname@example.org) and I put them in your project's page as son as possible.
Thanks. And then put a link of this page into your project's website.
The information about "BLACKJACK" are provided by the European Opendata Portal: CORDIS opendata.