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Finite Element Analysis

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Development and model form assessment of an automatic subject-specific vertebra reconstruction method.

Computers in biology and medicine
BACKGROUND: Current spine models for analog bench models, surgical navigation and training platforms are conventionally based on 3D models from anatomical human body polygon database or from time-consuming manual-labelled data. This work proposed a w...

Elasticity imaging using physics-informed neural networks: Spatial discovery of elastic modulus and Poisson's ratio.

Acta biomaterialia
Elasticity imaging is a technique that discovers the spatial distribution of mechanical properties of tissue using deformation and force measurements under various loading conditions. Given the complexity of this discovery, most existing methods appr...

Two-stage neural network via sensitivity learning for 2D photonic crystal bandgap maximization.

Applied optics
We propose a two-stage neural network method to maximize the bandgap of 2D photonic crystals. The proposed model consists of a fully connected deep feed-forward neural network (FNN) and U-Net, which are employed, respectively, to generate the shape f...

A strategy to formulate data-driven constitutive models from random multiaxial experiments.

Scientific reports
We present a test technique and an accompanying computational framework to obtain data-driven, surrogate constitutive models that capture the response of isotropic, elastic-plastic materials loaded in-plane stress by combined normal and shear stresse...

Crack control optimization of basement concrete structures using the Mask-RCNN and temperature effect analysis.

PloS one
In order to enhance the mitigation of crack occurrence and propagation within basement concrete structures, this research endeavors to propose an optimization methodology grounded in the Mask Region-based Convolutional Neural Network (Mask-RCNN) and ...

Prediction of Bone Healing around Dental Implants in Various Boundary Conditions by Deep Learning Network.

International journal of molecular sciences
Tissue differentiation varies based on patients' conditions, such as occlusal force and bone properties. Thus, the design of the implants needs to take these conditions into account to improve osseointegration. However, the efficiency of the design p...

An intelligent fault detection approach for digital integrated circuits through graph neural networks.

Mathematical biosciences and engineering : MBE
To quickly and accurately realize the fault diagnosis of analog circuits, this paper introduces the graph neural network method and proposes a fault diagnosis method for digital integrated circuits. The method filters the signals present in the digit...

Tendon Stress Estimation from Strain Data of a Bridge Girder Using Machine Learning-Based Surrogate Model.

Sensors (Basel, Switzerland)
Prestressed girders reduce cracking and allow for long spans, but their construction requires complex equipment and strict quality control. Their accurate design depends on a precise knowledge of tensioning force and stresses, as well as monitoring t...

Analysis of Friction Noise Mechanism in Lead Screw System of Autonomous Vehicle Seats and Dynamic Instability Prediction Based on Deep Neural Network.

Sensors (Basel, Switzerland)
This study investigated the squeal mechanism induced by friction in a lead screw system. The dynamic instability in the friction noise model of the lead screw was derived through a complex eigenvalue analysis via a finite element model. A two degree ...

Predicting trabecular arrangement in the proximal femur: An artificial neural network approach for varied geometries and load cases.

Journal of biomechanics
Machine learning (ML) and deep learning (DL) approaches can solve the same problems as the finite element method (FEM) with a high degree of accuracy in a fraction of the required time, by learning from previously presented data. In this work, the bo...