CARDIOVASCULAR ENGINEERING

AIM

The aim of this course is to examine the anatomical and physiological foundations of the cardiovascular system from an engineering perspective, with a particular focus on congenital heart diseases and the structural–functional consequences of pathological anatomy. The course seeks to develop students’ ability to critically analyze contemporary scientific literature, interpret anatomical information derived from different imaging data modalities, and integrate this knowledge into surgical planning, numerical modeling, and computational fluid dynamics approaches. In addition, the course introduces data-driven decision-support concepts and fundamental analytical methods used in cardiovascular engineering.

CONTENT

This course contains; Physics-Based Models in Cardiovascular Systems (Lumped Models, CFD, FEA),Introduction to Cardiovascular Engineering, Physiology, and Digital Twins,Cardiovascular Diseases as Nonlinear Dynamical Systems,Physiological Feedback Mechanisms and System Coupling,Mathematical Modeling of Cardiovascular Physiology,Structures and Dynamic Behavior,Data-Driven Modeling and System Identification in Physiology,Physics-Based Models in Cardiovascular Systems (Lumped Models, CFD, FEA),Hybrid Digital Twins: Integrating Physiology, Physics, and Data,Digital Twins for Clinical Decision Support,Case Studies in Cardiovascular Physiology and Disease Modeling,Case Studies in Cardiovascular Physiology and Disease Modeling,Student Project Presentations,Student Project Presentations,Model Validation, Reliability, and Regulation in Cardiovascular Systems.

1. 1

   Explain cardiovascular physiology and pathology using nonlinear system concepts such as feedback, coupling, and time-dependent behavior.

2. 2

   Formulate clinically relevant cardiovascular problems as mathematical and computational models.

3. 3

   Integrate clinical knowledge, physiological data, and modeling assumptions into a coherent digital twin framework.

4. 4

   Apply data-driven and physics-informed modeling approaches to analyze cardiovascular system behavior.

5. 5

   Interpret model outputs in the context of clinical decision support and communicate results effectively.

WEEKLY PLAN

1. WEEK 0

   Physics-Based Models in Cardiovascular Systems (Lumped Models, CFD, FEA)

2. WEEK 1

   Introduction to Cardiovascular Engineering, Physiology, and Digital Twins

3. WEEK 2

   Cardiovascular Diseases as Nonlinear Dynamical Systems

4. WEEK 3

   Physiological Feedback Mechanisms and System Coupling

5. WEEK 4

   Mathematical Modeling of Cardiovascular Physiology

6. WEEK 5

   Structures and Dynamic Behavior

7. WEEK 6

   Data-Driven Modeling and System Identification in Physiology

8. WEEK 7

   Physics-Based Models in Cardiovascular Systems (Lumped Models, CFD, FEA)

9. WEEK 8

   Hybrid Digital Twins: Integrating Physiology, Physics, and Data

10. WEEK 9

    Digital Twins for Clinical Decision Support

11. WEEK 10

    Case Studies in Cardiovascular Physiology and Disease Modeling

12. WEEK 11

    Case Studies in Cardiovascular Physiology and Disease Modeling

13. WEEK 12

    Student Project Presentations

14. WEEK 13

    Student Project Presentations

15. WEEK 14

    Model Validation, Reliability, and Regulation in Cardiovascular Systems

ASSESSMENT

• Rate of Midterm Exam to Success50%
• Rate of Final Exam to Success50%

WORKLOAD

READING

• Medical Physiology – Boron & Boulpaep Cardiovascular Mathematics – Quarteroni, Veneziani, Vergara Cardiovascular Engineering – Tuan Vo-Dinh
• The course will be delivered through lectures, physiology-driven modeling discussions, case-based examples, and project-oriented learning. Emphasis is placed on understanding system behavior, model structure, and data integration rather than on software-specific training. Selected journal articles and instructor-provided materials covering cardiovascular physiology, nonlinear systems, digital twins, and data-driven modeling.

TEACHING STAFF

• Assist.Prof. Kevser Banu KÖSECOORDINATOR
• Assist.Prof. Kevser Banu KÖSE