Multiscale modeling is a systematic approach for the development of high-fidelity models of complex systems. However, multiscale models are often extremely computationally demanding, which precludes their use for practical applications. In this article, we introduce a computational framework for scale-bridging combined with an algorithm to automatically and adaptively replace at-scale models within a multiscale model hierarchy with surrogate models in order to reduce the computational cost of multiscale simulations. A standalone module is introduced and it is responsible for the on-the-fly construction and evaluation of surrogate models within the framework. Such an approach allows multiscale models to easily incorporate surrogate models with minimal code modifications. We employ the framework to construct a multiscale model of 1,3,5-trinitrohexahydro-s-triazine, in which a continuum finite element macroscale solver acquires equation of state through evaluation of a microscale dissipative particle dynamics model. We utilize the model for the simulation of a Taylor impact experiment and demonstrate that the error in the solution incurred by the dynamic use of surrogate models is controllable. Furthermore, we show that the use of surrogate models leads to a reduction in computational cost of between 1/20 and 1/5000 compared to a simulation evaluated without the surrogate modeling approach. In addition, we present a high-resolution simulation of a Taylor impact experiment, which is intractable without surrogate models. We illustrate how the dynamic nature of surrogate model evaluation in these simulations, while reducing computational cost, also increases load imbalance. Finally, we end with a discussion on how the inherent variability in these simulations may constitute a challenge for the current high performance computer systems given their static nature.
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