A heterogeneous computing approach to coupled simulation and machine-learning deployment for high-speed flows

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 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, a Legion-API endowed with a CUDA code generator, and Python-based ML algorithms in 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.