Multi‐fidelity data fusion through parameter space reduction with applications to automotive engineering

Romor, Francesco; Tezzele, Marco; Mrosek, Markus; Othmer, Carsten; Rozza, Gianluigi

doi:10.1002/nme.7349

Cited by 3 publications

(5 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CFD methodology [25][26][27][28][29][30][31][32] citations (52,19,12,40,3,6,5,20) CFD tool [33] citations (16) CFD and experiment [34] citations (14) multicopter dynamics and experiment [35] citations (35) Different hyperparameters mesh size [36][37][38] citations (1,60,55)…”

Section: Different Algorithmsmentioning

confidence: 99%

“…Different Algorithms mathematical models [12,[39][40][41][42] citations (17,5,40,5,35) measurement tools [43,44] citations (43,11) Different hyperparameters mesh size [45][46][47] citations (2,1,13) convergence condition and simulated objects [10] citations (1) iteration limit [48] citations (25)…”

Section: Mechanical Engineeringmentioning

confidence: 99%

“…In reference [28], the doublet-lattice method and CFD-based flutter calculation are used to obtain LF and HF data, respectively. In mechanical engineering, different mathematical models (e.g., different finite element models [39,42] and surrogate models [12,40,41]) or different measuring tools (e.g., different strain sensors [43] and different wind-turbine-specific aero-servo-elastic computer simulators [44]) are commonly used to obtain MF data. For other fields, mainly in biomedical and economic fields, the methods of acquiring multi-fidelity data are listed in Table 1.…”

Section: Multi-fidelity Data From Different Algorithmsmentioning

confidence: 99%

“…In this validation, MF datasets are artificially constructed to obtain datasets. Here, we assumed an ideal function as Equation (12) to compute the true values.…”

Section: Multi-fidelity Data Acquisition and Processingmentioning

confidence: 99%

“…The systematic exposition and validation of multi-fidelity data learning methods in other fields have been extensively reviewed [11][12][13]. However, within the realm of materials science, there remains a notable absence of an overview and validation of multi-fidelity data learning methods.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Exploring Multi-Fidelity Data in Materials Science: Challenges, Applications, and Optimized Learning Strategies

Wang,

Liu,

Chen

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Machine learning techniques offer tremendous potential for optimizing resource allocation in solving real-world problems. However, the emergence of multi-fidelity data introduces new challenges. This paper offers an overview of the definition, applications, data preprocessing methodologies, and learning approaches associated with multi-fidelity data. To validate the algorithms, we examine three widely-used learning methods relevant to multi-fidelity data through the design of multi-fidelity datasets that encompass various types of noise. As we expected, employing multi-fidelity data learning methods yields better results compared to solely using high-fidelity data learning methods. Additionally, considering the inherent various types of noise within datasets, the comprehensive correction strategy proves to be the most effective. Moreover, multi-fidelity learning methods facilitate effective decision-making processes by enabling the combination of datasets from various sources. They extract knowledge from lower fidelity data, improving model accuracy compared to models solely relying on high-fidelity data.

show abstract

Section: Different Algorithmsmentioning

confidence: 99%

Section: Mechanical Engineeringmentioning

confidence: 99%

Section: Multi-fidelity Data From Different Algorithmsmentioning

confidence: 99%

“…In this validation, MF datasets are artificially constructed to obtain datasets. Here, we assumed an ideal function as Equation (12) to compute the true values.…”

Section: Multi-fidelity Data Acquisition and Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Exploring Multi-Fidelity Data in Materials Science: Challenges, Applications, and Optimized Learning Strategies

Wang,

Liu,

Chen

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

A Local Approach to Parameter Space Reduction for Regression and Classification Tasks

Romor,

Tezzele,

Rozza

2024

J Sci Comput

View full text Add to dashboard Cite

Parameter space reduction has been proved to be a crucial tool to speed-up the execution of many numerical tasks such as optimization, inverse problems, sensitivity analysis, and surrogate models’ design, especially when in presence of high-dimensional parametrized systems. In this work we propose a new method called local active subspaces (LAS), which explores the synergies of active subspaces with supervised clustering techniques in order to carry out a more efficient dimension reduction in the parameter space. The clustering is performed without losing the input–output relations by introducing a distance metric induced by the global active subspace. We present two possible clustering algorithms: K-medoids and a hierarchical top–down approach, which is able to impose a variety of subdivision criteria specifically tailored for parameter space reduction tasks. This method is particularly useful for the community working on surrogate modelling. Frequently, the parameter space presents subdomains where the objective function of interest varies less on average along different directions. So, it could be approximated more accurately if restricted to those subdomains and studied separately. We tested the new method over several numerical experiments of increasing complexity, we show how to deal with vectorial outputs, and how to classify the different regions with respect to the LAS dimension. Employing this classification technique as a preprocessing step in the parameter space, or output space in case of vectorial outputs, brings remarkable results for the purpose of surrogate modelling.

show abstract

Multifidelity Methodology for Reduced-Order Models with High-Dimensional Inputs

Mufti,

Perron,

Mavris

2024

AIAA Journal

View full text Add to dashboard Cite

In the early stages of aerospace design, reduced-order models (ROMs) are crucial for minimizing computational costs associated with using physics-rich field information in many-query scenarios requiring multiple evaluations. The intricacy of aerospace design demands the use of high-dimensional design spaces to capture detailed features and design variability accurately. However, these spaces introduce significant challenges, including the curse of dimensionality, which stems from both high-dimensional inputs and outputs necessitating substantial training data and computational effort. To address these complexities, this study introduces a novel multifidelity, parametric, and nonintrusive ROM framework designed for high-dimensional contexts. It integrates machine learning techniques for manifold alignment and dimension reduction—employing proper orthogonal decomposition and model-based active subspace—with multifidelity regression for ROM construction. Our approach is validated through two test cases: the 2D RAE 2822 airfoil and the 3D NASA CRM wing, assessing various fidelity levels, training data ratios, and sample sizes. Compared to the single-fidelity principal component–active subspace (PCAS) method, our multifidelity solution offers improved cost-accuracy benefits and achieves better predictive accuracy with reduced computational demands. Moreover, our methodology outperforms the manifold-aligned ROM method by 50% in handling scenarios with large input dimensions, underscoring its efficacy in addressing the complex challenges of aerospace design.

show abstract

Multi‐fidelity data fusion through parameter space reduction with applications to automotive engineering

Cited by 3 publications

References 54 publications

Exploring Multi-Fidelity Data in Materials Science: Challenges, Applications, and Optimized Learning Strategies

Exploring Multi-Fidelity Data in Materials Science: Challenges, Applications, and Optimized Learning Strategies

A Local Approach to Parameter Space Reduction for Regression and Classification Tasks

Multifidelity Methodology for Reduced-Order Models with High-Dimensional Inputs

Contact Info

Product

Resources

About