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German Aerospace Center

Academic institutioneurope · de
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Research library329linked papers
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Selected work

Representative Papers

Constrained Multi-Objective Bayesian Optimization with Application to Aircraft Design

Jun 20, 2022AIAA AVIATION 2022 Forum

Bayesian optimization (BO) methods for computationally expensive, nonlinearly constrained multi-objective optimization problems—such as aircraft conceptual design—often suffer from ill-conditioning in multi-objective acquisition functions, leading to unstable surrogate updates and poor convergence. Method: This paper extends the SEGOMOE framework by introducing a novel regularization mechanism directly into the multi-objective acquisition function, synergistically integrating Kriging surrogates, a Mixture-of-Experts (MoE) architecture, and an enhanced SEGO algorithm. Contribution/Results: The proposed approach systematically alleviates the trade-off between ill-conditioning and convergence in constrained multi-objective BO. Empirical evaluation on aircraft design tasks demonstrates that it achieves high-quality Pareto fronts using only 5% of the function evaluations required by NSGA-II, significantly improving the efficiency of identifying high-fidelity, low-cost compromise solutions.

8 citationsRead paper

Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies

Oct 09, 2024ION GNSS+, The International Technical Meeting of the Satellite Division of The Institute of Navigation

To address unreliable GNSS positioning in vehicular systems caused by real-world interference, this study systematically evaluates supervised (CNN, LSTM, Transformer) and pseudo-label-based unsupervised learning for interference signal classification, using large-scale real-world measurements from German highways and Austrian Alpine roads. Its key contributions include: (i) the first empirical validation of pseudo-labeling unsupervised learning in large-scale realistic vehicular GNSS scenarios; (ii) identification of cross-regional environmental discrepancies as a critical constraint on model generalization; and (iii) proposal of a synergistic adaptation framework integrating anomaly detection (Isolation Forest), domain adaptation (DANN), and time-frequency-domain data augmentation. Experimental results show a classification accuracy of 98.2%, with pseudo-labeling achieving 92% of supervised method performance; DANN improves cross-scenario F1-score by 37%, significantly mitigating data distribution shift.

3 citationsRead paper

MEmilio -- A high performance Modular EpideMIcs simuLatIOn software for multi-scale and comparative simulations of infectious disease dynamics

Feb 11, 2026

This work proposes a unified, modular, high-performance simulation framework to address the fragmentation in current infectious disease modeling ecosystems, which hinders cross-model comparison and deployment across model types, spatial scales, and computational platforms. For the first time, the framework integrates compartmental models, meta-population models, and agent-based models within a single architecture, enabling multi-scale and comparable epidemic dynamics simulations. By standardizing representations of spatial, demographic, and mobility data, coupling a high-performance C++ core with a Python interface, and incorporating uncertainty quantification and parameter inference tools, the framework supports seamless deployment—from laptops to high-performance computing environments—significantly lowering barriers to reuse and accelerating the development of simulation-driven epidemic response capabilities.

2 citationsRead paper

Surrogate-Based Optimization of System Architectures Subject to Hidden Constraints

Jul 27, 2024AIAA AVIATION FORUM AND ASCEND 2024

This work addresses the challenge of implicit constraints—manifested as evaluation failures—arising from unreliable physics-based simulations in system architecture optimization. To tackle this, we propose a surrogate modeling framework that integrates probabilistic feasibility prediction with Bayesian optimization. Methodologically, we introduce a novel hybrid discrete Gaussian process to model the Probability of Validity (PoV), coupled with an interior-point selection strategy based on a minimum PoV threshold; the framework natively supports hierarchical design variables and multi-objective optimization. Our approach achieves the first successful solution for a jet engine architecture optimization task with a 50% simulation failure rate. Across multiple synthetic benchmarks and real-world case studies, it significantly improves convergence robustness and optimization success rate. The implementation is publicly available as the SBArchOpt Python library.

2 citationsRead paper

Rate-Adaptive Protograph-Based MacKay-Neal Codes

Feb 01, 2025IEEE Transactions on Information Theory

Rate adaptation is required for high-throughput wireless/optical links employing binary modulation, fixed blocklength, and fixed inner-code constraints. Method: This paper proposes a protograph-based rate-adaptive MacKay-Neal (MN) code design. An outer distribution matcher controls the overall code rate, while an inner coupled protograph LDPC code forms a nonlinear concatenated structure; a multi-channel equivalent communication model is established. Density evolution and shape analysis of the normalized logarithmic asymptotic input–output weight distribution are jointly employed to eliminate error-floor-prone code ensembles during design. Contribution/Results: The scheme achieves performance within 1 dB of the Shannon limit across a wide code-rate range (0.2–0.9) using only a single LDPC protograph ensemble. It significantly enhances rate flexibility and hardware reusability without compromising error-floor mitigation or throughput efficiency.

1 citations1 influentialRead paper
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