Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows.

ABSTRACT: 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;

SUBMITTER: Zanardi I 

PROVIDER: S-EPMC10509218 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature