A physics-informed deep learning framework for modeling of coronary in-stent restenosis

Biomechanics and Modeling in Mechanobiology, vol. 23, pp. 615–629

Authors 5

1. Jianye Shi corresponding Aachen Institute of Applied Mechanics

   RWTH Aachen University

   Affiliation as printed

   Institute of Applied Mechanics, RWTH Aachen University, Aachen, Germany. jianye.shi@ifam.rwth-aachen.de

   Institute of Applied Mechanics, RWTH Aachen University, Aachen, Germany

2. Kiran Manjunatha Aachen Institute of Applied Mechanics

   RWTH Aachen University

   Affiliation as printed

   Institute of Applied Mechanics, RWTH Aachen University, Aachen, Germany

3. Marek Behr Aachen Chair for Computational Analysis of Technical Systems (CATS)

   RWTH Aachen University

   Affiliation as printed

   Chair for Computational Analysis of Technical Systems, RWTH Aachen University, Aachen, Germany

4. Felix Jan Vogt Aachen Department of Cardiology, Pulmonology

   RWTH Aachen University

   Affiliation as printed

   Department of Cardiology, Pulmonology, Intensive Care and Vascular Medicine, RWTH Aachen University, Aachen, Germany

5. Stefanie Reese Aachen Institute of Applied Mechanics

   RWTH Aachen University

   Affiliation as printed

   Institute of Applied Mechanics, RWTH Aachen University, Aachen, Germany