Real‐time biomechanics using the finite element method and machine learning: Review and perspective

2020 ◽  
Author(s):  
Renzo Phellan ◽  
Bahe Hachem ◽  
Julien Clin ◽  
Jean‐Marc Mac‐Thiong ◽  
Luc Duong
Keyword(s):  
Machine Learning ◽  
Finite Element Method ◽  
Finite Element ◽  
Real Time ◽  
The Finite Element Method ◽  
Element Method

scholarly journals Real-time biomechanical modeling of the liver using Machine Learning models trained on Finite Element Method simulations

2020 ◽  
Vol 143 ◽  
pp. 113083 ◽  
Author(s):  
Oscar J. Pellicer-Valero ◽  
María José Rupérez ◽  
Sandra Martínez-Sanchis ◽  
José D. Martín-Guerrero
Keyword(s):  
Machine Learning ◽  
Finite Element Method ◽  
Finite Element ◽  
Real Time ◽  
Biomechanical Modeling ◽  
Learning Models ◽  
Machine Learning Models ◽  
Element Method

scholarly journals Finite element approach to Bragg edge neutron strain tomography

2020 ◽  
Vol 60 ◽  
Author(s):  
Riya Aggarwal ◽  
Mike Meylan ◽  
Bishnu Lamichhane ◽  
Chris Wensrich
Keyword(s):  
Machine Learning ◽  
Finite Element Method ◽  
Finite Element ◽  
Gaussian Processes ◽  
Elastic Strain ◽  
Neutron Imaging ◽  
The Finite Element Method ◽  
Neutron Transmission ◽  
Ray Transform ◽  
Element Method

A number of techniques and applications in neutron imaging that exploit wavelength resolved measurements have been developed recently. One such technique, known as energy resolved neutron imaging, receives ample attention because of its capability to not only visualise but to also quantify physical attributes with spatial resolution. The objective of this article is to develop a reconstruction algorithm for elastic strain tomography from Bragg edge neutron transmission strain images obtained from a pulsed neutron beam with high resolution. This technique has several advantages over those using monochromatic neutron beams from continuous sources; for example, finer wavelength resolution. In contrast to the conventional radon based computed tomography, wherein neutron transmission revolves around the inversion of the longitudinal ray transform that has uniqueness issues, the reconstruction in the proposed algorithm is based on the least squares approach, constrained by an equilibrium formulated through the finite element method. References B. Abbey, S. Y. Zhang, W. J. J. Vorster, and A. M. Korsunsky. Feasibility study of neutron strain tomography. Proc. Eng., 1\penalty 0 (1):185–188, 2009. doi:10.1016/j.proeng.2009.06.043. R. Aggarwal, M. H. Meylan, B. P. Lamichhane, and C. M. Wensrich. Energy resolved neutron imaging for strain reconstruction using the finite element method. J. Imag., 6(3):13, 2020. doi:10.3390/jimaging6030013. J. N. Hendriks, A. W. T. Gregg, C. M. Wensrich, A. S. Tremsin, T. Shinohara, M. Meylan, E. H. Kisi, V. Luzin, and O. Kirsten. Bragg-edge elastic strain tomography for in situ systems from energy-resolved neutron transmission imaging. Phys. Rev. Mat., 1:053802, 2017. doi:10.1103/PhysRevMaterials.1.053802. C. Jidling, J. Hendriks, N. Wahlstrom, A. Gregg, T. B. Schon, C. Wensrich, and A. Wills. Probabilistic modelling and reconstruction of strain. Nuc. Inst. Meth. Phys. Res. B, pages 141–155, 2018. doi:10.1016/j.nimb.2018.08.051. W. R. B. Lionheart and P. J. Withers. Diffraction tomography of strain. Inv. Prob., 31:045005, 2015. doi:10.1088/0266-5611/31/4/045005. C. E. Rasmussen and C. K. I. Williams. Gaussian processes for machine learning. MIT Press, 2006. URL https://mitpress.mit.edu/books/gaussian-processes-machine-learning. C. M. Wensrich, E. Kisi, V. Luzin, and O. Kirstein. Non-contact measurement of the stress within granular materials via neutron diffraction. AIP Conf. Proc., 1542:441–444, 2013. doi:10.1063/1.4811962. R. Woracek, J. Santisteban, A. Fedrigo, and M. Strobl. Diffraction in neutron imaging–-a review. Nuc. Inst. Meth. Phys. Res. A, 878:141–158, 2018. doi:10.1016/j.nima.2017.07.040.

scholarly journals Survey of Finite Element Method-Based Real-Time Simulations

2019 ◽  
Vol 9 (14) ◽  
pp. 2775 ◽  
Author(s):  
Dragan Marinkovic ◽  
Manfred Zehn
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Real Time ◽  
The Finite Element Method ◽  
Typical Application ◽  
Time Simulation ◽  
Simulation Based ◽  
Rapid Pace ◽  
Real Time Simulations ◽  
Element Method

The finite element method (FEM) has deservedly gained the reputation of the most powerful, highly efficient, and versatile numerical method in the field of structural analysis. Though typical application of FE programs implies the so-called “off-line” computations, the rapid pace of hardware development over the past couple of decades was the major impetus for numerous researchers to consider the possibility of real-time simulation based on FE models. Limitations of available hardware components in various phases of developments demanded remarkable innovativeness in the quest for suitable solutions to the challenge. Different approaches have been proposed depending on the demands of the specific field of application. Though it is still a relatively young field of work in global terms, an immense amount of work has already been done calling for a representative survey. This paper aims to provide such a survey, which of course cannot be exhaustive.

Simulation of tuning graphene plasmonic behaviors by ferroelectric domains for self-driven infrared photodetector applications

Nanoscale ◽  
2019 ◽  
Vol 11 (43) ◽  
pp. 20868-20875 ◽  
Author(s):  
Junxiong Guo ◽  
Yu Liu ◽  
Yuan Lin ◽  
Yu Tian ◽  
Jinxing Zhang ◽  
...  
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Interfacial Effect ◽  
Infrared Photodetector ◽  
The Finite Element Method ◽  
Ferroelectric Domains ◽  
Element Method

We propose a graphene plasmonic infrared photodetector tuned by ferroelectric domains and investigate the interfacial effect using the finite element method.

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