Process Systems Engineering;
Max Planck Institute for Dynamics of Complex Technical Systems;
Magdeburg D-39106;
Germany;
Process Systems Engineering;
Otto-von-Guericke University Magdeburg;
Magdeburg D-39106;
Germany;
PSE for SPEED Co.;
Ltd.;
Allerod DK 3450;
Denmark;
Department of Chemical and Biomolecular Engineering;
Korea Advanced Institute of Science and Technology(KAIST);
Daejeon 34141;
Republic of Korea;
Data-driven; Surrogate model; Machine learning; Hybrid modeling; Material design; Process optimization;