Deep learning in frequency domain for inverse identification of nonhomogeneous material properties

Liu, Yizhe; Chen, Yuli; Ding, Bin

doi:10.1016/j.jmps.2022.105043

Cited by 14 publications

(2 citation statements)

References 60 publications

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“…While approaches such as SHAP quantify the contribution of each design variable to the performance, we would also like to highlight that these approaches can be limited when the design space becomes too large. In these cases, unsupervised feature reduction and selection approaches such as Principal Component Analysis (PCA) can be used in tandem with SHAP analysis 30,31 .…”

Section: Investigating the Mechanical Performance Via Surrogate Modelingmentioning

confidence: 99%

Accelerating the design of lattice structures using machine learning

Gongora,

Friedman,

Newton

et al. 2024

Sci Rep

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Lattices remain an attractive class of structures due to their design versatility; however, rapidly designing lattice structures with tailored or optimal mechanical properties remains a significant challenge. With each added design variable, the design space quickly becomes intractable. To address this challenge, research efforts have sought to combine computational approaches with machine learning (ML)-based approaches to reduce the computational cost of the design process and accelerate mechanical design. While these efforts have made substantial progress, significant challenges remain in (1) building and interpreting the ML-based surrogate models and (2) iteratively and efficiently curating training datasets for optimization tasks. Here, we address the first challenge by combining ML-based surrogate modeling and Shapley additive explanation (SHAP) analysis to interpret the impact of each design variable. We find that our ML-based surrogate models achieve excellent prediction capabilities (R2 > 0.95) and SHAP values aid in uncovering design variables influencing performance. We address the second challenge by utilizing active learning-based methods, such as Bayesian optimization, to explore the design space and report a 5 × reduction in simulations relative to grid-based search. Collectively, these results underscore the value of building intelligent design systems that leverage ML-based methods for uncovering key design variables and accelerating design.

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Section: Investigating the Mechanical Performance Via Surrogate Modelingmentioning

confidence: 99%

Accelerating the design of lattice structures using machine learning

Gongora,

Friedman,

Newton

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Especially for the applications of elasticity imaging, great efforts are made to make the ML and DL powerful tools with high accuracy and computational efficiency [14][15][16][17][18]. A variety of deep learning network architectures are used for material parameter identification from different perspectives [19][20][21][22], such as conditional Generative Adversarial Networks [14], physics-informed neural network [19], and convolutional neural network [20]. In the framework of DL, we are merely concerned with imageto-image translation.…”

Section: Introductionmentioning

confidence: 99%

Toward Improved Accuracy in Quasi-Static Elastography Using Deep Learning

Mei,

Deng,

Zhao

et al. 2024

CMES

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Elastography is a non-invasive medical imaging technique to map the spatial variation of elastic properties of soft tissues. The quality of reconstruction results in elastography is highly sensitive to the noise induced by imaging measurements and processing. To address this issue, we propose a deep learning (DL) model based on conditional Generative Adversarial Networks (cGANs) to improve the quality of nonhomogeneous shear modulus reconstruction. To train this model, we generated a synthetic displacement field with finite element simulation under known nonhomogeneous shear modulus distribution. Both the simulated and experimental displacement fields are used to validate the proposed method. The reconstructed results demonstrate that the DL model with synthetic training data is able to improve the quality of the reconstruction compared with the well-established optimization method. Moreover, we emphasize that our DL model is only trained on synthetic data. This might provide a way to alleviate the challenge of obtaining clinical or experimental data in elastography. Overall, this work addresses several fatal issues in applying the DL technique into elastography, and the proposed method has shown great potential in improving the accuracy of the disease diagnosis in clinical medicine.

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Finite Element Model Updating for Material Model Calibration: A Review and Guide to Practice

Chen,

Starman,

Halilovič

et al. 2024

Arch Computat Methods Eng

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Finite element model updating (FEMU) is an advanced inverse parameter identification method capable of identifying multiple parameters in a material model through one or a few well-designed material tests. The method has become more mature thanks to the widespread use of full-field measurement techniques, such as digital image correlation. Proper application of FEMU requires extensive expertise. This paper offers a review of FEMU and a guide to practice. It also presents FEMU-DIC, an open-source software package. We conclude by discussing the challenges and opportunities in this field with the intent of inspiring future research.

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Deep learning in frequency domain for inverse identification of nonhomogeneous material properties

Cited by 14 publications

References 60 publications

Accelerating the design of lattice structures using machine learning

Accelerating the design of lattice structures using machine learning

Toward Improved Accuracy in Quasi-Static Elastography Using Deep Learning

Finite Element Model Updating for Material Model Calibration: A Review and Guide to Practice

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