Syndicate content

Nonlocal Methods for Arbitrary Data Sources - NoMADS. H2020 RISE project

Funded by the European Commission HORIZON 2020 programme, reference H2020-MSCA-RISE-2017. Contract (GA) number 777826 NoMADS. Start Date: March 1, 2018. End Date: February 28, 2022.

NoMADS is an international research project consisting of 16 universities and 7 industrial partners. The project is funded by the European Commission within the Marie Skłodowska-Curie Research and Innovation Staff Exchange action (MSCA-RISE).

The main goal of NoMADS is to build a large multidisciplinary network of universities and companies to fill the current gaps between theory and applications of nonlocal methods. In the last years, the trend in data sciences was shifting from model-driven towards data-driven methods. A number of recent data-driven methods make use of self-similarity of patterns within the data by relating information that is not necessarily in a close proximity. These methods are termed as ‘nonlocal methods’. The aim of NoMADs is to significantly increase the understanding and applicability of nonlocal methods in a wide range of applications. Our long-term vision is to discover fundamental mathematical principles for the characterization of nonlocal operators, the development of new robust and efficient algorithms, and the implementation of those in high quality software products for real-world applications.