Multi-fidelity surrogate modeling through hybrid machine learning for biomechanical and finite element analysis of soft tissues

Sajjadinia, Seyed Shayan; Carpentieri, Bruno; Shriram, Duraisamy; Holzapfel, Gerhard

doi:10.1016/j.compbiomed.2022.105699

Cited by 10 publications

(2 citation statements)

References 52 publications

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“…Different Algorithms mathematical models [49] citations (104) mathematical models and simulation tools [50] citations (27) Different hyperparameters main or simplified numerical model [51] citations (1) mesh size [52] citations (66) physical constraints [53] citations (1)…”

Section: Othersmentioning

confidence: 99%

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

Wang,

Liu,

Chen

et al. 2023

Applied Sciences

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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.

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Section: Othersmentioning

confidence: 99%

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

Wang,

Liu,

Chen

et al. 2023

Applied Sciences

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show abstract

“…However, in general, several extensive complex FE simulations are required during the lifespan of a mechanical system, which is often impractical. Low-/mid-fidelity FEAs, which are updated on the actual condition of the system via experimental measurements and update themselves rapidly and effectively, have been offered as alternatives to comprehensive simulations [18], [19]. Bartilson et al [20] explored natural frequency and mode shape sensitivities in structures with at least one plane of symmetry and distinct natural frequencies.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Surrogate Finite Element Model for Estimating Dynamic Response of Mechanical Systems

2023

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An efficient approach for improving the predictive understanding of dynamic mechanical system variability is developed in this work. The approach requires low model assessment time through the fitting of surrogate models. ML-based surrogate algorithms for finite element analysis (FEA) are developed in this study to accelerate FEA and prevent rerunning complex simulations. The research begins with an overview of the recent novelties in ML algorithms applied to finite element (FE) and other physics-based computational schemes. To predict the time-varying response variables, that is, the displacement of a twodimensional truss structure, a surrogate FE technique based on ML algorithms is developed. In this work, several ML regression algorithms, including decision trees (DTs) and deep neural networks, are developed, and their efficacies are compared. In this study, the ML-based surrogate FE models are able to effectively predict the response of the truss structure in two dimensions over the entire structure. Extreme gradientboosting DTs provide more precise outcomes and outperform other ML algorithms.INDEX TERMS Surrogate modeling, finite element analysis, mechanical system analysis, machine learning, artificial neural networks, random forest trees, gradient boosting regression trees, adaptive boosting trees.

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Hybrid Data‐Driven and Numerical Modeling of Articular Cartilage

Sajjadinia,

Carpentieri,

Holzapfel

2024

Big Data Analysis and Artificial Intelligence for Medical Sciences

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Multi-fidelity surrogate modeling through hybrid machine learning for biomechanical and finite element analysis of soft tissues

Cited by 10 publications

References 52 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 Machine Learning-Based Surrogate Finite Element Model for Estimating Dynamic Response of Mechanical Systems

Hybrid Data‐Driven and Numerical Modeling of Articular Cartilage

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