Supermodeling of tumor dynamics with parallel isogeometric analysis solver
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We show that it is possible to obtain reliable prognoses about cancer dynamics by creating the supermodel of cancer, which consists of several coupled instances (the sub-models) of a generic cancer model, developed with isogeometric analysis. Its integration with real data can be achieved by employing a prediction/correction learning scheme focused on fitting several values of coupling coefficients between sub-models, instead of matching scores (even hundreds) of tumor model parameters as it is in the classical data adaptation techniques. We show that the isogeometric analysis is a proper tool to develop a generic computer model of cancer, which can be a computational framework for developing high-quality supermodels. We believe that the latent fine-grained tumor features, e.g., microscopic processes and other unpredictable events accompanying its proliferation not included in the model (that is, not included in direct way in the mathematical model), are present in incoming real data and will still influence in indirect way tumor dynamics.
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