Dieuwertje Alblas - MIA
Christian Amend - MIA
Riccardo Bardin - MACS
Vincent Bosboom - MACS
Giacomo Cristinelli - MIA
Sven Dummer - MIA
Elena Giamatteo - MACS
Leonardo del Grande - MIA
source: http://www.malinc.se/math/trigonometry/geocentrismen.php - Heeringa - MIA
Lucas Jansen Klomp - MIA
Muhammad Hamza Khalid - MACS
Nishant Kumar - MACS
Kaifang Liu - MACS
Xiangyi Meng - MACS
Floor van Maarschalkerwaart - MIA
Nida Mir - MIA / MDI-TNW
Hongliang Mu - MAST
Kevin Redosado - 3MS
Julian Suk - MIA
Hannah van Susteren - MIA
Johanna Tengler - MIA
Mei Vaish - MIA
Jens de Vries - MAST
Weihao Yan - MIA
Alexander Wierzba - MAST
Fengna Yan - MACS

Scientific Machine Learning (SciML)


Scientific Machine Learning (SciML) is an emerging area in computational science that investigates the synergistic integration of scientific computing and machine learning, with the goal of combining the strengths and compensating the weaknesses of both. In particular, SciML aims to (i) use numerical models for the improvement of established machine learning techniques, (ii) use data-driven machine learning techniques to assist numerical simulations, and (iii) create new efficient, reliable, and robust methodologies by employing aspects from both approaches. The SciML research in the MIA group focuses on:


People working on this subject within MCS are:

Staff:

PhD