Learning the Dynamics of Concentration Fields in Vascular Stenosis with Deep Hidden 116 Models

Understanding the dynamics of blood flow is crucial in the context of cardiovascular health and disease. The dynamics of the blood flow can be a significant parameter for the development of decision support systems to enable early detection and accurate diagnosis of coronary artery diseases. Uncovering the underlying dynamics from high-dimensional data generated from experiments is a highly complex problem at the intersection of artificial intelligence and applied mathematics. Deep Hidden 116 Models can be used to learn the underlying dynamics without additional physical knowledge.In this work, the potential of Deep Hidden 116 Models to model the clinically relevant dynamics of blood flow is investigated. The experiments consider the use case of stenosis in two-dimensional spatial space. Based on the learned dynamics, the concentration field can be approximated accurately, indicating that the dynamics are learned correctly. Additionally, we examine the capability of the model to extrapolate the learned dynamics for unknown time intervals.

  • Published in:
    IEEE International Conference on Big Data
  • Type:
    Inproceedings
  • Authors:
    Kador, Rebecca; Schneider, Helen; Biesner, David; Dellen, Babette; Sifa, Rafet
  • Year:
    2023

Citation information

Kador, Rebecca; Schneider, Helen; Biesner, David; Dellen, Babette; Sifa, Rafet: Learning the Dynamics of Concentration Fields in Vascular Stenosis with Deep Hidden 116 Models, IEEE International Conference on Big Data, 2023, https://www.computer.org/csdl/proceedings-article/bigdata/2023/10386807/1TUPjiOG8Bq, Kador.etal.2023a,

Associated Lamarr Researchers

lamarr institute person Biesner David - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

David Biesner

Autor to the profile
Prof. Dr. Rafet Sifa

Prof. Dr. Rafet Sifa

Principal Investigator Hybrid ML to the profile