Scientific Machine Learning


Research, improve, develop and apply fast and efficient state-of-the-art data-driven and numerical algorithms, based on machine learning (ML) and scientific computing (SC), that are robust, reliable, scalable and fast on modern computing systems to solve high-dimensional problems related to uncertainty quantification, model-based simulation, machine learning / artificial intelligence and surrogate modelling utilizing high-performance computing. The research focus on theoretical developments as well as practical applications.

Peer-Reviewed Publications related to date-driven methodologies and applications

Highlights to appear.