This work explores the powerful synergy between artificial intelligence and mechanistic modeling in advancing cardiac electromechanics simulations. As these simulations become increasingly complex, AI-based techniques—such as Gaussian Processes and Neural Networks—are proving essential for enhancing both computational efficiency and physiological realism.
The presentation will cover state-of-the-art methods, including Polynomial Chaos Expansion (PCE), Bayesian inference for uncertainty quantification, AI-enabled inverse modeling for cardiac electrophysiology, and machine learning approaches for parameter estimation and model calibration. A key focus will be the bidirectional integration between AI and physics-based models: AI accelerates simulations and improves predictive performance, while mechanistic models provide structural constraints that enhance AI training and generalization.
In addition, we address data assimilation strategies that fuse experimental and clinical data with computational models, ensuring that simulations reflect real-world physiological behavior and deliver clinically relevant outcomes. Emphasis will be placed on reducing computational cost without compromising fidelity—crucial for enabling efficient workflows in both research and clinical settings. Through practical examples, the presentation will demonstrate successful applications of AI-driven tools in cardiac modeling and highlight emerging challenges and future directions in this evolving field.