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Life Operators is a self-evolving framework for multiscale life modelling in medical AI, defining perception, evolution, and generation operators to represent patient states and changes under interventions.
The paper presents the Frame Kernel Method, a novel multiscale operator learning approach for surrogate modeling of PDEs that uses kernel frame approximations and achieves higher accuracy than popular neural operators while enabling multiscale decomposition.