Physics-based modeling has played a crucial role in advancing the understanding of neurophysiological systems including, but not limited to, cardiovascular, neurovascular, pulmonary, and gastrointestinal systems. Machine learning has also made its inroads in medical research, mainly to analyze clinical or experimental data. Currently, there has been a movement to combine the two powerful approaches to help better understand the underlying physics and mechanisms in these complex physiological systems.
With the advances in the power of modern computers, we can now generate a significant amount of data from physics-based models of various systems. However, there are still some bottlenecks such as computational time, limited grid resolution, post-processing large simulation data sets, pre-processing, model parameter estimations, and uncertainty quantifications. Machine learning has been gaining more attention as a potential tool to alleviate such limitations that arise in physics-based modelling. Therefore the goal of this article collection is to present recent advancement in physics-based models of physiological systems in the era of machine learning.
We welcome submissions related to but not limited to the following sub-topics:
• Advances in post-processing and analysis of physiological systems with machine learning techniques on data generated by simulating physics-based models
• Advances in the use of machine learning techniques to accelerate large-scale simulations of physics-based models of physiological systems
• Advances in development of multiscale models of physiological systems
• Advances in development of reduced-order models of complex physiological systems
The topic editors and the topic coordinator declare no conflict of interest.
Physics-based modeling has played a crucial role in advancing the understanding of neurophysiological systems including, but not limited to, cardiovascular, neurovascular, pulmonary, and gastrointestinal systems. Machine learning has also made its inroads in medical research, mainly to analyze clinical or experimental data. Currently, there has been a movement to combine the two powerful approaches to help better understand the underlying physics and mechanisms in these complex physiological systems.
With the advances in the power of modern computers, we can now generate a significant amount of data from physics-based models of various systems. However, there are still some bottlenecks such as computational time, limited grid resolution, post-processing large simulation data sets, pre-processing, model parameter estimations, and uncertainty quantifications. Machine learning has been gaining more attention as a potential tool to alleviate such limitations that arise in physics-based modelling. Therefore the goal of this article collection is to present recent advancement in physics-based models of physiological systems in the era of machine learning.
We welcome submissions related to but not limited to the following sub-topics:
• Advances in post-processing and analysis of physiological systems with machine learning techniques on data generated by simulating physics-based models
• Advances in the use of machine learning techniques to accelerate large-scale simulations of physics-based models of physiological systems
• Advances in development of multiscale models of physiological systems
• Advances in development of reduced-order models of complex physiological systems
The topic editors and the topic coordinator declare no conflict of interest.
