Surrogate neural network model for sensitivity analysis and uncertainty quantification of the mechanical behavior in the optical lens-barrel assembly

Shahane, Shantanu; Guleryuz, Erman; Abueidda, Diab W.; Lee, Allen; Liu, Joe; Yu, Xin; Chiu, Raymond; Korić, Seid; Aluru, N. R.; Ferreira, Placid M.

doi:10.1016/j.compstruc.2022.106843

Cited by 17 publications

(4 citation statements)

References 29 publications

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“…Over the past decade, deep learning (DL) has been used in many fields, including fluid mechanics, , solid mechanics, , materials science, composite/additive manufacturing, − and sensitivity analysis and uncertainty quantification . In these applications, training of the DNN is performed by minimizing the distance between the prediction and training data.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Data-Driven Deep-Learning Surrogate Model for Frontal Polymerization in Dicyclopentadiene

Liu,

Abueidda,

Vyas

et al. 2024

J. Phys. Chem. B

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Frontal polymerization (FP) is a self-sustaining curing process that enables rapid and energy-efficient manufacturing of thermoset polymers and composites. Computational methods conventionally used to simulate the FP process are time-consuming, and repeating simulations are required for sensitivity analysis, uncertainty quantification, or optimization of the manufacturing process. In this work, we develop an adaptive surrogate deep-learning model for FP of dicyclopentadiene (DCPD), which predicts the evolution of temperature and degree of cure orders of magnitude faster than the finite-element method (FEM). The adaptive algorithm provides a strategy to select training samples efficiently and save computational costs by reducing the redundancy of FEMbased training samples. The adaptive algorithm calculates the residual error of the FP governing equations using automatic differentiation of the deep neural network. A probability density function expressed in terms of the residual error is used to select training samples from the Sobol sequence space. The temperature and degree of cure evolution of each training sample are obtained by a 2D FEM simulation. The adaptive method is more efficient and has a better prediction accuracy than the random sampling method. With the well-trained surrogate neural network, the FP characteristics (front speed, shape, and temperature) can be extracted quickly from the predicted temperature and degree-of-cure fields.

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Section: Introductionmentioning

confidence: 99%

Adaptive Data-Driven Deep-Learning Surrogate Model for Frontal Polymerization in Dicyclopentadiene

Liu,

Abueidda,

Vyas

et al. 2024

J. Phys. Chem. B

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“…Some of the widely used modeling techniques include neural networks [58][59][60], kriging [61], radial basis functions [62], polynomial chaos expansion [63], support vector regression [64], etc. Datadriven surrogates are used for global [65] and multi-objective design [66] and statistical analysis [67], and they are often combined with machine learning methods [68]. Their fundamental drawback is related to the curse of dimensionality [69], which hinders their applicability in higher-dimensional parameter spaces.…”

Section: Introductionmentioning

confidence: 99%

Globalized Knowledge-Based, Simulation-Driven Antenna Miniaturization Using Domain-Confined Surrogates and Dimensionality Reduction

2023

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The design of contemporary antenna systems encounters multifold challenges, one of which is a limited size. Compact antennas are indispensable for new fields of application such as the Internet of Things or 5G/6G mobile communication. Still, miniaturization generally undermines electrical and field performance. When attempted using numerical optimization, it turns into a constrained problem with costly constraints requiring electromagnetic (EM) simulations. At the same time, due to the parameter redundancy of compact antennas, size reduction poses a multimodal task. In particular, the achievable miniaturization rate heavily depends on the starting point, while identifying a suitable starting point is a challenge on its own. These issues indicate that miniaturization should be addressed using global optimization methods. Unfortunately, the most popular nature-inspired algorithms cannot be applied for solving size reduction tasks because of their inferior computational efficacy and difficulties in handling constraints. This work proposes a novel methodology for the globalized size reduction of antenna structures. Our methodology is a multi-stage knowledge-based procedure, initialized with the detection of the approximate location of the feasible region boundary, followed by the construction of a dimensionality-reduced metamodel and global optimization thereof; the last stage is the miniaturization-oriented local refinement of geometry parameters. For cost reduction, the first stages of the procedure are realized with the use of a low-fidelity EM antenna model. Our approach is verified using four broadband microstrip antennas and benchmarked against multi-start local search as well as nature-inspired methods. Superior size reduction rates are demonstrated for all considered cases while maintaining reasonably low computational costs.

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“…Recently, rapid growth in the utilization of deep learning (DL) and data‐driven modeling has been seen in computational solid mechanics 4‐13 . While DL offers a platform for rapid inference, it needs a large number of data points to learn the multifaceted correlations between the outputs and inputs.…”

Section: Introductionmentioning

confidence: 99%

Enhanced physics‐informed neural networks for hyperelasticity

Abueidda

Korić

Guleryuz

et al. 2022

Numerical Meth Engineering

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Physics‐informed neural networks have gained growing interest. Specifically, they are used to solve partial differential equations governing several physical phenomena. However, physics‐informed neural network models suffer from several issues and can fail to provide accurate solutions in many scenarios. We discuss a few of these challenges and the techniques, such as the use of Fourier transform, that can be used to resolve these issues. This paper proposes and develops a physics‐informed neural network model that combines the residuals of the strong form and the potential energy, yielding many loss terms contributing to the definition of the loss function to be minimized. Hence, we propose using the coefficient of variation weighting scheme to dynamically and adaptively assign the weight for each loss term in the loss function. The developed PINN model is standalone and meshfree. In other words, it can accurately capture the mechanical response without requiring any labeled data. Although the framework can be used for many solid mechanics problems, we focus on three‐dimensional (3D) hyperelasticity, where we consider two hyperelastic models. Once the model is trained, the response can be obtained almost instantly at any point in the physical domain, given its spatial coordinates. We demonstrate the framework's performance by solving different problems with various boundary conditions.

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Surrogate neural network model for sensitivity analysis and uncertainty quantification of the mechanical behavior in the optical lens-barrel assembly

Cited by 17 publications

References 29 publications

Adaptive Data-Driven Deep-Learning Surrogate Model for Frontal Polymerization in Dicyclopentadiene

Adaptive Data-Driven Deep-Learning Surrogate Model for Frontal Polymerization in Dicyclopentadiene

Globalized Knowledge-Based, Simulation-Driven Antenna Miniaturization Using Domain-Confined Surrogates and Dimensionality Reduction

Enhanced physics‐informed neural networks for hyperelasticity

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