A team from the University of Florida and Johns Hopkins University introduces DIMON, a machine learning framework that integrates diffeomorphic mapping of geometries into operator learning, drastically reducing computation time for PDE solutions and paving the way for real-time cardiac digital twins.

Key points

• Introduction of DIMON, integrating diffeomorphic mapping into operator learning for PDEs
• Use of LDDMM to reduce geometric parameterization to as few as 64 dimensions
• Achieves training on standard laptops in minutes versus 12–24 hours on CPU clusters
• Demonstrated on cardiac electrophysiology, Laplace’s equation, and reaction-diffusion PDEs
• Enables real-time cardiac digital twins for surgical guidance

Why it matters: By embedding geometric transformations directly into machine-learning solvers, DIMON shifts PDE modeling from hours of computation to near-instant results on modest hardware. This advance accelerates real-time cardiac digital twin applications, improving surgical decision support and opening new avenues for rapid simulation in engineering and biomedical research.

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