Data-driven approximations of ordinary differential equations offer a promising alternative to classical methods of discovering a dynamical system model, particularly in complex systems lacking explicit first principles. This paper focuses on a complex system whose dynamics is described with a system of such equations, coupled through a complex network. Numerous real-world systems, including financial, social, and neural systems, belong to this class of dynamical models. We propose essential elements for approximating these dynamical systems using neural networks, including necessary biases and an appropriate neural architecture. Emphasizing the differences from static supervised learning, we advocate for evaluating generalization beyond classical assumptions of statistical learning theory. To estimate confidence in prediction during inference time, we introduce a dedicated null model. By studying various complex network dynamics, we demonstrate that the neural approximations of dynamics generalize across complex network structures, sizes, and statistical properties of inputs. Our comprehensive framework enables accurate and reliable deep learning approximations of high-dimensional, nonlinear dynamical systems.

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