Opendata, web and dolomites

dataFlow SIGNED

dataFlow: A Data-driven Fluid Flow Solving Platform

Total Cost €

0

EC-Contrib. €

0

Partnership

0

Views

0

Project "dataFlow" data sheet

The following table provides information about the project.

Coordinator
TECHNISCHE UNIVERSITAET MUENCHEN 

Organization address
address: Arcisstrasse 21
city: MUENCHEN
postcode: 80333
website: www.tu-muenchen.de

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Germany [DE]
 Total cost 149˙500 €
 EC max contribution 149˙500 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2018-PoC
 Funding Scheme ERC-POC
 Starting year 2019
 Duration (year-month-day) from 2019-06-01   to  2020-11-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    TECHNISCHE UNIVERSITAET MUENCHEN DE (MUENCHEN) coordinator 149˙500.00

Map

 Project objective

With the recent breakthrough of deep learning methods, we currenty see the advent of employing this methodology in the context of physical simulations. Such simulations are widely used in numerous industrial fields, starting from car and airplane manufacturers, over computer graphics and animations to medical blood flow simulations. The market for computer simulations is currently exceeding 15 billion USD world wide, with rising trends, and 3 billion spent in Europe alone. A significant fraction of these simulations focuses purely on solving various forms of the Navier-Stokes equations. While right now virtually all of these simulations use traditional solvers, we estimate than only a few years from now there will be a significant fraction of deep learning powered solvers.

Thus, we are at the right point in time to lay the foundations for commercializing the technology of deep learning for fluid simulations. The goal of this PoC project is to develop a first commercial flow solver based on deep learning that can predict fluid flow solutions almost instantly using a pre-trained model. This project will enable the team of Prof. Thuerey to mature the algorithms developed as part of the ERC Starting Grant realflow, and turn them into the basis of a marketable product. The initial models will be thoroughly tested and validated, in order to satisfy industrial requirements for reliability and accuracy. In addition, this PoC aims for establishing a platform for flow data collection, interface standards, and trained models. This platform will be developed in conjunction to the deep-learning powered flow solving application, and provide research connections and publicity in parallel to it.

Are you the coordinator (or a participant) of this project? Plaese send me more information about the "DATAFLOW" 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 (fabio@fabiodisconzi.com) 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 "DATAFLOW" are provided by the European Opendata Portal: CORDIS opendata.

More projects from the same programme (H2020-EU.1.1.)

CANCEREVO (2019)

Deciphering and predicting the evolution of cancer cell populations

Read More  

TotipotentZygotChrom (2020)

Mechanisms of chromatin organization and reprogramming in totipotent mammalian zygotes

Read More  

WEB DATA OPP (2020)

New opportunities to enhance or extend (mobile) web survey data and get better insights

Read More  
lastchecktime (2020-11-30 7:05:26) correctly updated