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We present a data-driven modeling strategy to overcome improperly modeleddynamics for systems exhibiting complex spatio-temporal behaviors. We propose aDeep Learning framework to resolve the differences between the true dynamics ofthe system and the dynamics given by a model of the system that is eitherinaccurately or inadequately described. Our machine learning strategy leveragesdata generated from the improper system model and observational data from theactual system to create a neural network to model the dynamics of the actualsystem. We evaluate the proposed framework using numerical solutions obtainedfrom three increasingly complex dynamical systems. Our results show that oursystem is capable of learning a data-driven model that provides accurateestimates of the system states both in previously unobserved regions as well asfor future states. Our results show the power of state-of-the-art machinelearning frameworks in estimating an accurate prior of the system's truedynamics that can be used for prediction up to a finite horizon.

Bridging the Gap: Machine Learning to Resolve Improperly Modeled Dynamics