arXiv subjects

Jeroen Wackers

Publications and source records attributed to Jeroen Wackers.

3 recordsLinked to original sources

arXiv2026

A Machine Learning Enabled MDO for Bio-Inspired Autonomous Underwater Gliders

The preliminary design of AUGs is intrinsically challenging due to the strong coupling between the external hydrodynamic shape, the hydrostatic balance, the structural integrity, and internal packaging constraints. This complexity is further amplified for bio-inspired configurations, whose rich geometric parametrizations lead to high-dimensional design spaces that are difficult to explore using conventional optimization approaches. This work presents a ML-enabled bi-level multidisciplinary design optimization (MDO) framework for the performance-driven design of a manta-ray-inspired AUG. At the upper level, hydrodynamically efficient external geometries are explored in a reduced design space obtained through physics-driven parametric model embedding, which identifies a low-dimensional latent representation directly correlated with the lift, drag, and pressure distributions. At the lower level, a constrained internal sizing problem determines the minimum feasible empty weight by accounting for structural, hydrostatic, geometric, and payload constraints. To render the resulting bi-level problem computationally tractable, a multi-fidelity surrogate-based optimization strategy is adopted, combining low- and high-fidelity hydrodynamic models with stochastic radial basis function surrogates and adaptive Bayesian sampling. The framework enables efficient exploration of the coupled design space while rigorously managing model uncertainty and computational cost. The optimized configurations exhibit a 14.7\% improvement in maximum hydrodynamic efficiency and a 12.8\% reduction in empty weight relative to the baseline design, while satisfying all disciplinary constraints. These results demonstrate that the integration of physics-driven dimensionality reduction and multi-fidelity machine learning enables scalable and physically consistent MDO of complex bio-inspired underwater vehicles.

cs.CE↗

arXiv2025

Extending Parametric Model Embedding with Physical Information for Design-space Dimensionality Reduction in Shape Optimization

Design-space dimensionality reduction is essential to mitigate the cost of high-fidelity simulation-based optimization, especially when dealing with high-dimensional geometric parameterizations. Traditional linear techniques, such as principal component analysis, are widely used but often neglect the physical response of the system and lack invertibility to the design space, i.e., the ability to reconstruct the original design parameters from a reduced representation. This work introduces two physics-aware extensions of the parametric model embedding (PME) framework, aimed at generating reduced representations that incorporate physical information while maintaining analytical backmapping. The first, physics-informed PME (PI-PME), combines geometric and physical variability; the second, physics-driven PME (PD-PME), relies solely on physical responses. The proposed methods enable the construction of interpretable and physically relevant reduced spaces that can be used for design-space exploration, surrogate modeling, and optimization. The approach is demonstrated on multiple engineering configurations, including airfoils, propellers, gliders, and hulls, showing its ability to capture performance-relevant directions and preserve parametric consistency. The methodology is offline and non-intrusive, compatible with low-fidelity simulations, and requires only a modest number of samples to ensure variance convergence.

math.OC↗

arXiv2022

A Multi-Fidelity Active Learning Method for Global Design Optimization Problems with Noisy Evaluations

A multi-fidelity (MF) active learning method is presented for design optimization problems characterized by noisy evaluations of the performance metrics. Namely, a generalized MF surrogate model is used for design-space exploration, exploiting an arbitrary number of hierarchical fidelity levels, i.e., performance evaluations coming from different models, solvers, or discretizations, characterized by different accuracy. The method is intended to accurately predict the design performance while reducing the computational effort required by simulation-driven design (SDD) to achieve the global optimum. The overall MF prediction is evaluated as a low-fidelity trained surrogate corrected with the surrogates of the errors between consecutive fidelity levels. Surrogates are based on stochastic radial basis functions (SRBF) with least squares regression and in-the-loop optimization of hyperparameters to deal with noisy training data. The method adaptively queries new training data, selecting both the design points and the required fidelity level via an active learning approach. This is based on the lower confidence bounding method, which combines performance prediction and associated uncertainty to select the most promising design regions. The fidelity levels are selected considering the benefit-cost ratio associated with their use in the training. The method's performance is assessed and discussed using four analytical tests and three SDD problems based on computational fluid dynamics simulations, namely the shape optimization of a NACA hydrofoil, the DTMB 5415 destroyer, and a roll-on/roll-off passenger ferry. Fidelity levels are provided by both adaptive grid refinement and multi-grid resolution approaches. Under the assumption of a limited budget of function evaluations, the proposed MF method shows better performance in comparison with the model trained by high-fidelity evaluations only.

math.OC↗