Work

Bio-inspired multi-dimensional deep fusion learning for predicting dynamical aerospace propulsion systems

Year: 2024

Type: article

Abstract: Rapid and precise forecasting of dynamical systems is critical to ensuring safe aerospace missions. Previous forecasting research has primarily concentrated on global trend analysis using full-scale inputs. However, time series arising from real-world applications such as aerospace propulsion, exhibit a distinct dynamical periodicity over a limited timeframe. Here we develop a deep learning model, TimeWaves, to capture both global trends and local variations, through 3D spectrum-oriented interval extraction from an integrated viewpoint of biological perceptions. Specifically, a shared parameter fusion algorithm is employed to effectively integrate Fourier and Wavelet analyses, providing full and sliced 1D sequences to form 2D tensors that can be seamlessly processed by parameter-efficient inception blocks. Additionally, a dual-way learning workflow using TwinBlock, inspired by the cooperative behavior of visual cells, is implemented to enhance perception of dynamical multi-scale features at a reduced computational cost. TimeWaves demonstrates reliable and robust performance in predicting rocket combustion instability, a key challenge in the aerospace industry.

Source: Communications Engineering

DOI: 10.1038/s44172-024-00327-9

Authors: Michael Qian Vergnolle, Eastman Z. Y. Wu, Yanan Sui, Qian Wang

Institutions: Tsinghua University, Beijing Computational Science Research Center

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,090

Funder: National Natural Science Foundation of China