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
Call for proposal
See other projects for this call