Towards Deep Learning Surrogate for the Forward Problem in Electrocardiology: A Scalable Alternative to Physics-Based Models
arXiv:2512.13765v2 Announce Type: replace-cross Abstract: The forward problem in electrocardiology, computing body surface potentials from cardiac electrical activity, is traditionally solved using physics-based models such as the bidomain or monodomain equations. While accurate, these approaches are computationally expensive, limiting their use in real-time and large-scale clinical applications. We propose a proof-of-concept deep learning (DL) framework as an efficient surrogate for forward solvers. The model adopts a time-dependent, attention-based sequence-to-sequence architecture to predic
The increasing computational power and advancement in deep learning architectures are enabling researchers to tackle complex scientific problems previously constrained by traditional physics-based models.
This development indicates a significant trend towards using AI as a high-performance surrogate for computationally intensive scientific simulations, potentially accelerating research and development in fields like medicine and engineering.
The ability to rapidly and efficiently solve electrocardiology's forward problem using AI could dramatically improve the speed and accessibility of cardiac diagnostics and treatment planning, moving from slow, expensive simulations to real-time, AI-driven insights.
- · Healthcare technology companies
- · Medical AI developers
- · Cardiologists and researchers
- · Patients needing cardiac diagnostics
- · Developers of traditional physics-based simulation software
- · Companies heavily invested in high-performance computing infrastructure solely f
Deep learning models become widely adopted as surrogates for other computationally expensive physics-based simulations in various scientific and engineering disciplines.
This shift democratizes access to complex simulations, enabling smaller research groups and less resource-intensive institutions to conduct advanced R&D.
The reliance on AI surrogates could lead to a 'black box' problem in critical applications, necessitating new standards for explainability, validation, and regulatory oversight of AI in scientific discovery.
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