CoMetaPNS: Continually Meta-learning Personalized Neural Surrogates for Cardiac Electrophysiology

arXiv·low signal

CoMetaPNS applies continual meta-learning to personalize neural surrogate models for cardiac electrophysiology simulations, targeting the twin challenges of model personalization and computational cost. The continual-meta-learning framing for adapting surrogates to individual instances is methodologically interesting, but the application is narrow medical-domain work with limited crossover to general AI/ML practice.