Efficient multi-fidelity reduced-order modeling for nonlinear flutter prediction

X Wang, S Song, X Peng, W Zhang - Aerospace Science and Technology, 2024 - Elsevier
Flutter prediction is an important part of aircraft design. However, high-fidelity predictions for
transonic flutter are difficult to make because of the associated computational costs. This …

Model order reduction method based on (r) POD-ANNs for parameterized time-dependent partial differential equations

F Cheng, H Xu, X Feng - Computers & Fluids, 2022 - Elsevier
In this paper, we propose a non-intrusive data-driven model order reduction method, also
known as (r) POD-ANNs model order reduction method. In this method,(r) POD priori …

Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models

M Tomasetto, A Manzoni, F Braghin - ar** from the system's
measured outputs and inputs to all of the system's states. Analytical and empirical tools exist …

Cluster regression model for flow control

N Arya, AG Nair - Physics of Fluids, 2024 - pubs.aip.org
In the realm of big data, discerning patterns in nonlinear systems affected by external control
inputs is increasingly challenging. Our approach blends the coarse-graining strengths of …

Parametric PDE Control with Deep Reinforcement Learning and Differentiable L0-Sparse Polynomial Policies

N Botteghi, U Fasel - arxiv preprint arxiv:2403.15267, 2024 - arxiv.org
Optimal control of parametric partial differential equations (PDEs) is crucial in many
applications in engineering and science. In recent years, the progress in scientific machine …

[HTML][HTML] Novel high-safety aeroengine performance predictive control method based on adaptive tracking weight

C Qian, H SHENG, J ZHANG, LI Jiacheng - Chinese Journal of Aeronautics, 2024 - Elsevier
Increasing attention has been attracted to the dynamic performance and safety of advanced
performance predictive control systems of the next-generation aeroengine. The latest …