The paper discusses the challenges of integrating machine learning (ML) and artificial intelligence (AI) methods with computational fluid dynamics (CFD) simulations, particularly when operating at high-performance computing scales. It presents a solution using the open-source libraries OpenFOAM, SmartSim, and SmartRedis.
The key highlights include:
A loosely coupled, data-centric architecture that enables a clean separation of concerns between the CFD and ML components. This allows for modularity and flexibility when examining different ML frameworks.
Integration of SmartSim and SmartRedis into OpenFOAM to facilitate data exchange, synchronization, and computation on heterogeneous hardware. This includes the development of an OpenFOAM function object to handle the communication with the SmartRedis database.
Three example use cases that demonstrate the capabilities of the proposed approach:
The examples showcase the versatility of the CFD+ML workflows that can be achieved using the integration of OpenFOAM, SmartSim, and SmartRedis. The provided code and documentation serve as educational resources and starting points for the computational science community to develop more complex CFD+ML applications.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Tomislav Mar... at arxiv.org 04-25-2024
https://arxiv.org/pdf/2402.16196.pdfDeeper Inquiries