The purpose of the Competence Center on Simulation and Big Data is the fostering of an effective collaboration between the different LIP groups working on these areas and to boost the capability to exploit the existing expertise both internally and externally, towards the university and the industry. The different LIP groups have a vast range of competences in data analysis and simulation tools, including physics models, Monte Carlo generators, detector simulation tools, big-data handling techniques and data mining. The ability to fully benefit from such competences requires achieving critical mass, a coordinated training program, the exploitation of synergies between groups and a clear identification of the key areas where we can contribute in a competitive way.
The competence center started its activities in 2017 and the first priorities were the identification of the technical competences mastered by the LIP members in these two areas, establishing communication and discussion forums, starting a training program and establishing an action plan for the next few years.
Related links
Photos
-
Berry: A code for the differentiation of Bloch wavefunctions from DFT calculations
-
Author(s): Leander Reascos, Fábio Carneiro, André Pereira, Nuno Filipe Castro, Ricardo Mendes Ribeiro
-
Submission: 2023-05-31, Acceptance: 2023-10-17, Publication: 2023-10-27
-
Reference: Computer Physics Communications 295 (2024) 108972
View publication
-
Quantum machine learning in HEP
-
Author(s): Bruna Salgado, Catarina Felgueiras
-
Submission: 2023-10-17, Acceptance: 2023-10-17, Publication: 2023-10-17
-
Reference: LIP-STUDENTS-23-16
View publication
-
Exploring parameter spaces with artificial intelligence and machine learning black-box optimization algorithms
-
Author(s): Fernando Abreu de Souza, Miguel Crispim Romão, Nuno Castro, Mehraveh Nikjoo, Werner Porod
-
Submission: 2022-07-04, Acceptance: 2023-01-10, Publication: 2023-02-06
-
Reference: Phys. Rev. D 107, 035004
View publication
-
Fitting a Collider in a Quantum Computer: Tackling the Challenges of Quantum Machine Learning for Big Datasets
-
Author(s): Miguel Caçador Peixoto, Nuno Filipe Castro, Miguel Crispim Romão, Maria Gabriela Jordão Oliveira, Inês Ochoa
-
Submission: 2022-11-06, Acceptance: 2022-11-06, Publication: 2022-11-06
-
Reference: arXiv:2211.03233