Dieuwertje Alblas - MIA
Riccardo Bardin - MACS
Vincent Bosboom - MACS
Nicoló Botteghi - MIA
Xiaoyu Cheng - MACS
Giacomo Cristinelli - MIA
Sven Dummer - MIA
Sagy Ephrati - MMS
Arnout Franken - MMS
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
Nida Mir - MIA / MDI-TNW
Hongliang Mu - MAST
Kevin Redosado - 3MS
Julian Suk - MIA
Hannah van Susteren - MIA
Elina Thibeua-Sutre - MIA
Alexander Wierzba - MAST
Jens de Vries - MAST
Fengna Yan - MACS
Weihao Yan - MIA

Scientific machine learning with uncertainty quantification

Scientific Machine Learning (SciML) is a recent emerging area in scientific computing that investigates the integration of physical modeling and simulation with their data-driven counterpart, aiming to combine the strengths and compensate for the weaknesses of both. In particular, SciML (i) uses numerical models for the improvement of established machine learning techniques, (ii) uses data-driven machine learning techniques to assist numerical simulations, and (iii) creates new high-performance computational methodologies by employing aspects from both sides. 

The MIA team in SciML has specialized interests in Bayesian learning for dynamical systems, physically consistent model reduction, and physics-informed deep learning. In particular, research efforts are being made to synergize deep learning and Gaussian processes to enable robust data-driven discovery of low-dimensional dynamics from various measurement data, while certifying computational reliability with an endowment of uncertainty quantification.