Inhaltszusammenfassung

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 Phy...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 Physics Models can be used to learn the underlying dynamics without additional physical knowledge.In this work, the potential of Deep Hidden Physics 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.» weiterlesen» einklappen

Autoren

Klassifikation

DFG Fachgebiet:
Informatik

DDC Sachgruppe:
Physik