Data-driven reduced order surrogate modeling for coronary in-stent restenosis
The demonstrated non-intrusive reduced order surrogate model proves to be a powerful tool for predicting ISR outcomes. Moreover, the proposed method lays the foundation for real-time simulations and optimization of PCI parameters.
Authors
- Felix Vogt (ORCID: https://orcid.org/0000-0001-7334-8006)
- Jianye Shi (ORCID: https://orcid.org/0000-0002-7855-2695)
- Stefanie Reese (ORCID: https://orcid.org/0000-0003-4760-8358)
- Kiran Manjunatha (ORCID: https://orcid.org/0000-0003-1757-7909)
Institutions
- RWTH Aachen University (DE)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2024-10-25
- DOI
- https://doi.org/10.1016/j.cmpb.2024.108466
- Citations
- 1
- Primary Topic
- Model Reduction and Neural Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.28
Funders
- Deutsche Forschungsgemeinschaft