Surrogation of High-Fidelity Simulation Software

The proposed innovation is a mesh-based graph neural network (GNN) framework for training high-fidelity surrogate models. The models are highly accurate and increase computational efficiency approximately 1-2 orders of magnitude faster than the software which generated the data. This would allow use of previously generated simulations to alleviate High-Performance Computing (HPC) resources, minimize the time-to-solution for engineering and science workflows, and increase the collaboration of various initiatives by providing a leaner alternative to simulations. The mesh-based GNN, or simply MeshGraphNetwork (MGN), is based on a pioneering publication from Google’s DeepMind project shown to accurately predict the dynamics of a wide range of physical systems, including those found within computational fluid dynamics (CFD). MGNs are a novel class of GNNs that directly operate on irregular meshes with arbitrary connectivity which have difficulty scaling on hardware accelerators. Due to the neural network basis of the MGNs components, it is suitable for acceleration on hardware commonly present in HPCs.

Machine learning solutions that model the continuous fields of a CFD scenario, or other system dynamics, solved by a simulation code such as FUN3D or Cart3D, will be invaluable to NASA as computing demands are projected to increase year over year for many types of high-fidelity simulations. These simulations are essential for all aspects of a technical workflow such as design, development, and testing. Our proposal is a general solution that can leverage existing tools and ultimately increase the operational compute capacity of an HPC system.

This proposal expands Anyar's ongoing research for the DoD related to Physics-Informed Neural Networks (PINN). Our DoD customers believe our MeshGraphNet library will fundamentally alter the speed of their penetration mechanics solutions for hydrodynamics problems. This proposal is a natural extension and expansion of our existing MGN library into fluid dynamics.

This is early/mid-stage (TRL 5) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

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