Inverse problem of photoelastic fringe mapping using neural networks

Measuring the forces that excite a structure into vibration is an important tool in modeling the system and investigating ways to reduce the vibration. However, determining the forces that have been applied to a vibrating structure can be a challenging inverse problem, even when the structure is instrumented with a large number of sensors. Previously, an artificial neural network was developed to identify the location of an impulsive force on a rectangular plate. In this research, the techniques were extended to plates of arbitrary shape. The principal challenge of arbitrary shapes is that some combinations of network outputs (x- and y-coordinates) are invalid. For example, for a plate with a hole in the middle, the network should not output that the force was applied in the center of the hole. Different methods of accommodating arbitrary shapes were investigated, including output space quantization and selecting the closest valid region.

Download Full-text

Identification of complex Bragg gratings (Apodized and chirped) using artificial neural networks (ANN) (inverse problem and ANN)

2006 Asia-Pacific Microwave Conference ◽

10.1109/apmc.2006.4429647 ◽

2006 ◽

Author(s):

A. Rostami ◽

A. Yazdanpanah-Goharrizi

Keyword(s):

Neural Networks ◽

Inverse Problem ◽

Artificial Neural Networks ◽

Bragg Gratings ◽

Artificial Neural

Download Full-text

Ultrasound Tomography Imaging of Defects Using Neural Networks

Neural Computation ◽

10.1162/neco.1992.4.5.758 ◽

1992 ◽

Vol 4 (5) ◽

pp. 758-771 ◽

Cited By ~ 2

Author(s):

Denis M. Anthony ◽

Evor L. Hines ◽

David A. Hutchins ◽

J. T. Mottram

Keyword(s):

Neural Networks ◽

Inverse Problem ◽

Artificial Neural Networks ◽

Longitudinal Wave ◽

Time Of Flight ◽

Simplified Model ◽

Gray Scale ◽

Ultrasound Tomography ◽

Tomography Imaging ◽

Ultrasound Propagation

Simulations of ultrasound tomography demonstrated that artificial neural networks can solve the inverse problem in ultrasound tomography. A highly simplified model of ultrasound propagation was constructed, taking no account of refraction or diffraction, and using only longitudinal wave time of flight (TOF). TOF data were used as the network inputs, and the target outputs were the expected pixel maps, showing defects (gray scale coded) according to the velocity of the wave in the defect. The effects of varying resolution and defect velocity were explored. It was found that defects could be imaged using time of flight of ultrasonic rays.

Download Full-text

Inverse problem of aircraft structural parameter estimation: application of neural networks

Inverse Problems in Science and Engineering ◽

10.1080/17415970600573411 ◽

2006 ◽

Vol 14 (4) ◽

pp. 351-363 ◽

Cited By ~ 7

Author(s):

P. M. Trivailo ◽

G. S. Dulikravich ◽

D. Sgarioto ◽

T. Gilbert

Keyword(s):

Neural Networks ◽

Inverse Problem ◽

Parameter Estimation ◽

Structural Parameter

Download Full-text

Determination of probabilistic parameters of concrete: solving the inverse problem by using artificial neural networks

Computers & Structures ◽

10.1016/s0045-7949(00)00073-0 ◽

2000 ◽

Vol 78 (1-3) ◽

pp. 497-503 ◽

Cited By ~ 9

Author(s):

E.M.R. Fairbairn ◽

N.F.F. Ebecken ◽

C.N.M. Paz ◽

F.-J. Ulm

Keyword(s):

Neural Networks ◽

Inverse Problem ◽

Artificial Neural Networks ◽

Artificial Neural

Download Full-text

Reconstruction of Depolarization Pattern in Myocardial Infarction Cases Using Two Cascaded Stages of Artificial Neural Networks

Journal of Physics Conference Series ◽

10.1088/1742-6596/2128/1/012016 ◽

2021 ◽

Vol 2128 (1) ◽

pp. 012016

Author(s):

Nihal A. Mabrouk ◽

Abdelreheem M. Khalifa ◽

Abdelmenem A. Nasser ◽

Moustafa H. Aly

Keyword(s):

Myocardial Infarction ◽

Neural Networks ◽

Inverse Problem ◽

Artificial Neural Networks ◽

Electrical Activity ◽

Heart Diseases ◽

Three Dimensional ◽

The Body ◽

Forward Model ◽

Artificial Neural

Abstract Our paper introduces a new technique for diagnosis of various heart diseases without the need of highly experts to investigate the electrocardiogram (ECG). Using the same electrodes of the ECG machine, it will be able to transmit directly the electrical activity inside the heart to a moving picture. Our technique is based on artificial intelligence algorithm using artificial neural networks (ANN). Finding the trans-membrane potential (TMP) inside the heart from the body surface potential (BSP) is known as the inverse problem of ECG. To have a unique solution for the inverse problem the data used should be obtained from a forward model. A three dimensional (3-D) model of cellular activation whole heart embedded in torso is simulated and solved using COMSOL Multiphysics software. In our previous paper, one ANN succeeded in displaying the wave propagation on the surface of a normal heart. In this paper, we used a configuration of ANNs to display different cases of heart with myocardial infarction (MI). To check the system accuracy, eight MI cases with different sizes and locations in the heart are simulated in the forward model. This configuration proved to be highly accurate in displaying each MI case -size and location- presenting the infarction as an area with no electrical activity.

Download Full-text

Inverse design of organic light-emitting diode structure based on deep neural networks

Nanophotonics ◽

10.1515/nanoph-2021-0434 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Sanmun Kim ◽

Jeong Min Shin ◽

Jaeho Lee ◽

Chanhyung Park ◽

Songju Lee ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Inverse Problem ◽

Optical Properties ◽

Extraction Efficiency ◽

Optimization Problems ◽

Light Extraction ◽

Light Extraction Efficiency ◽

Design Parameters ◽

Light Emitting

Abstract The optical properties of thin-film light emitting diodes (LEDs) are strongly dependent on their structures due to light interference inside the devices. However, the complexity of the design space grows exponentially with the number of design parameters, making it challenging to optimize the optical properties of multilayer LEDs with rigorous electromagnetic simulations. In this work, we demonstrate an artificial neural network that can predict the light extraction efficiency of an organic LED structure in 30 ms, which is ∼103 times faster than the rigorous simulation in a single-treaded execution with root-mean-squared error of 1.86 × 10−3. The effective inference time per structure is brought down to ∼0.6 μs with unaltered error rate with parallelization. We also show that our neural networks can efficiently solve the inverse problem – finding a device design that exhibits the desired light extraction spectrum – within the similar time scale. We investigate the one-to-many mapping issue of the inverse problem and find that the degeneracy can be lifted by incorporating additional emission spectra at different observing angles. Furthermore, the forward neural network is combined with a conventional genetic algorithm to address additional large-scale optimization problems including maximization of light extraction efficiency and minimization of angle dependent color shift. Our approach establishes a platform for tackling computation-heavy optimization tasks with one-time computational cost.

Download Full-text