Robust parameter design for rocket boosters using deep Gaussian process

doi:10.1016/j.ast.2025.110143

Robust parameter design for rocket boosters using deep Gaussian process

2025-06-01

0

PRE

AI

DOI：10.1016/j.ast.2025.110143

Original

Original request for help

Save

Abstract

En 中文

In aerospace engineering optimization, multiple correlated non-stationary responses (NSRs) are often produced. Ignoring the non-stationary characteristic of data may impact the prediction accuracy of response surface models and the reliability of optimization solutions. Traditional stationary Gaussian process model struggles to handle non-stationary data with limited samples. Recently, deep Gaussian process (DGP) has gained popularity in nonstationary models due to its ability to handle rapidly changing data. However, efficiently inferring the posterior distribution of DGP remains a significant challenge. This paper uses DGP surrogate model based on Bayesian inference to simulate non-stationary response surfaces and proposes the Chain Rule Hamiltonian Monte Carlo (CRHMC) algorithm to efficiently and accurately sample the posterior distribution. Additionally, a new multivariate quality loss function (MQLF) is introduced to optimize multiple correlated non-stationary responses and ensure the robustness of optimal design parameters. Numerical examples and a real rocket booster engineering case demonstrate the superiority of the proposed method in fast computation and robust optimization against other competing methods.

Keywords:

Non-stationary responses

Deep gaussian process

Robust parameter design

Rocket boosters

Quality design

Hamiltonian monte carlo

Robust parameter design for rocket boosters using deep Gaussian process

Abstract

Journal

Organization