Can I read Adaptive Physics-informed Neural Operator for Coarse-grained Non-equilibrium Flows on EtoBox?

Adaptive Physics-informed Neural Operator for Coarse-grained Non-equilibrium Flows by Zanardi, Ivan; Venturi, Simone; Panesi, Marco is a scholarly article available to read on EtoBox.

What is Adaptive Physics-informed Neural Operator for Coarse-grained Non-equilibrium Flows about?

This work proposes a new machine learning (ML)-based paradigm aiming to enhance the computational efficiency of non-equilibrium reacting flow simulations while ensuring compliance with the underlying physics. The framework combines dimensionality reduction and neural operators through a hierarchical and adaptive deep learning strategy to learn the solution of multi-scale coarse-grained governing equations for chemical kinetics. The proposed surrogate's architecture is structured as a tree, with leaf nodes representing separate neural operator blocks where physics is embedded in the form of multiple soft and hard constraints. The hierarchical attribute has two advantages: i) It allows the simplification of the training phase via transfer learning, starting from the slowest temporal scales; ii) It accelerates the prediction step by enabling adaptivity as the surrogate's evaluation is limited to the necessary leaf nodes based on the local degree of non-equilibrium of the gas. The model is applied to the study of chemical kinetics relevant for application to hypersonic flight, and it is tested here on pure oxygen gas mixtures. In 0-D scenarios, the proposed ML framework can adaptively

Author

Zanardi, Ivan; Venturi, Simone; Panesi, Marco

Published

2022

Language

EN