A heterogeneous computing approach to coupled simulation and machine-learning deployment for high-speed flows
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This work studies real-time integration of computational fluid dynamics (CFD) simulations and machine learning (ML) tasks, and its application to parameter estimation of high-speed multi-component flows in chemical propulsion systems. Our approach leverages implicit task-based parallelism through the Legion runtime ecosystem~\cite{Bauer} to efficiently execute expensive PDE solvers on GPUs and ML tasks on CPUs, on heterogeneous supercomputers. To achieve parallel performance without cumbersome implementation, we combine an in-house compressible flow solver written in Regent~\cite{Slaughter}, a Legion-API endowed with a CUDA code generator, and Python-based ML algorithms in Pygion~\cite{Pygion}, a Legion-API retaining the flexibility of Python while permitting the use of its immense ML ecosystem. In the application, the solver generates an ensemble of transient, high-speed, turbulent jets of multi-component mixtures. The ML tasks simultaneously extract subsets of the data and feed them to an ensemble of deep-neural networks for on-line training and for the Bayesian estimation of flow parameters. The influences of both the ensemble data size and the ensemble model size on the accuracy of estimation are discussed.
