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

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.