Deep Learning Surrogate for Electrocardiology: A Scalable Alternative
Analysis
This research explores using deep learning to create a surrogate model for the complex forward problem in electrocardiology. This approach potentially offers significant advantages in terms of computational speed and scalability compared to traditional physics-based models.
Key Takeaways
- •Deep learning is used to create a surrogate model for the electrocardiology forward problem.
- •The approach aims to improve computational efficiency and scalability.
- •This could lead to faster and more accessible cardiac simulations.
Reference
“The research focuses on a scalable alternative to physics-based models.”