Spatio-Temporal Methods for Data-driven Computer Animation and Simulation

Project description

New algorithms for machine learning of physical behavior in space and time

Numerical simulations are frequently required to study the behaviour of systems whose mathematical models are too complex to provide analytical solutions. In studying these phenomena, it is necessary to consider spatial dimensions that are difficult to compute and store in terms of the resources required to do so. The EU-funded SpaTe project aims to develop novel algorithms to infer spatio-temporal functions, allowing also for the construction of efficient representations that developers say will tame their complexity and high dimensionality. Ultimately, SpaTe will allow for a better understanding of the physical world around us and offer substantial practical applications spanning the range from social media apps to self-driving cars.

Fields of science

• natural sciences › computer and information sciences › artificial intelligence › machine learning
• natural sciences › computer and information sciences › artificial intelligence › computer vision
• engineering and technology › mechanical engineering › vehicle engineering › automotive engineering › autonomous vehicles

Programme(s)

• H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC)

Topic(s)

• ERC-2019-COG - ERC Consolidator Grant

Call for proposal

ERC-2019-COG
See other projects for this call

Funding Scheme

ERC-COG - Consolidator Grant

Host institution

TECHNISCHE UNIVERSITAET MUENCHEN

Address

Arcisstrasse 21
80333 Muenchen
Germany

Activity type

Higher or Secondary Education Establishments

EU contribution

€ 1 998 750

Beneficiaries (1)

TECHNISCHE UNIVERSITAET MUENCHEN

Germany

EU contribution

€ 1 998 750

Address

Arcisstrasse 21
80333 Muenchen

Activity type

Higher or Secondary Education Establishments