Monte Carlo simulation fused with target distribution modelling via deep reinforcement learning for automatic high-efficient photon distribution estimation

We have investigated a possibility of photon propagation into the human tissue model (skin, fat, and skeletal muscle) by Monte Carlo method using Matlab program. There were some parameters of each tissue layer effecting on the light packet, for instance the absorption coefficient, scattering coefficient, anisotropy factor and thickness. It was found that the photon distribution on the surface of the human tissue and photon penetration into the human tissue under the propagation of 100,000 photons were - 0.8580 cm to + 0.7030 cm (served as two detection points) and 0.7220 cm respectively. Therefore, the simulation result gave the photon penetration depth of 0.2220 cm at the skeletal muscle. These numbers could be primarily used as a standard for design and construction of the tissue diagnostic instrument.

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Distribution Estimation Using Concomitants of Order Statistics, with Application to Monte Carlo Simulation for the Bootstrap

Journal of the Royal Statistical Society Series B (Methodological) ◽

10.1111/j.2517-6161.1992.tb01899.x ◽

1992 ◽

Vol 54 (2) ◽

pp. 595-607

Author(s):

Kim-Anh Do ◽

Peter Hall

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Order Statistics ◽

Concomitants Of Order Statistics ◽

Distribution Estimation

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Efficient Monte Carlo Simulation for Pricing Variance Derivatives under Multi-Factor Stochastic Volatility Models

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.411-414.1089 ◽

2013 ◽

Vol 411-414 ◽

pp. 1089-1094

Author(s):

Jun Mei Ma ◽

Gui Ding Gu

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Stochastic Volatility ◽

High Efficiency ◽

Stochastic Volatility Models ◽

Control Variate ◽

Volatility Models ◽

High Efficient ◽

Efficient Control ◽

Speed Up

This paper studied the pricing of variance swap derivatives under the multi-factor stochastic volatility models by Monte Carlo simulation. Control variate technique was well used to reduce the variance of the simulation effectively. How to choose the high efficient control variate was also contained. Then the numerical results show the high efficiency of the speed up method. The pricing structure in the paper is also applicable for the valuation of other types of variance swaps and other financial derivatives under multi-factor models.

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Artificial Intelligence for Monte Carlo Simulation in Medical Physics

Frontiers in Physics ◽

10.3389/fphy.2021.738112 ◽

2021 ◽

Vol 9 ◽

Author(s):

David Sarrut ◽

Ane Etxebeste ◽

Enrique Muñoz ◽

Nils Krah ◽

Jean Michel Létang

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Monte Carlo Simulations ◽

Convolutional Neural Networks ◽

Medical Physics ◽

Nuclear Imaging ◽

Computation Time ◽

Distribution Estimation

Monte Carlo simulation of particle tracking in matter is the reference simulation method in the field of medical physics. It is heavily used in various applications such as 1) patient dose distribution estimation in different therapy modalities (radiotherapy, protontherapy or ion therapy) or for radio-protection investigations of ionizing radiation-based imaging systems (CT, nuclear imaging), 2) development of numerous imaging detectors, in X-ray imaging (conventional CT, dual-energy, multi-spectral, phase contrast … ), nuclear imaging (PET, SPECT, Compton Camera) or even advanced specific imaging methods such as proton/ion imaging, or prompt-gamma emission distribution estimation in hadrontherapy monitoring. Monte Carlo simulation is a key tool both in academic research labs as well as industrial research and development services. Because of the very nature of the Monte Carlo method, involving iterative and stochastic estimation of numerous probability density functions, the computation time is high. Despite the continuous and significant progress on computer hardware and the (relative) easiness of using code parallelisms, the computation time is still an issue for highly demanding and complex simulations. Hence, since decades, Variance Reduction Techniques have been proposed to accelerate the processes in a specific configuration. In this article, we review the recent use of Artificial Intelligence methods for Monte Carlo simulation in medical physics and their main associated challenges. In the first section, the main principles of some neural networks architectures such as Convolutional Neural Networks or Generative Adversarial Network are briefly described together with a literature review of their applications in the domain of medical physics Monte Carlo simulations. In particular, we will focus on dose estimation with convolutional neural networks, dose denoising from low statistics Monte Carlo simulations, detector modelling and event selection with neural networks, generative networks for source and phase space modelling. The expected interests of those approaches are discussed. In the second section, we focus on the current challenges that still arise in this promising field.

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Electron Scattering in Solids

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100133618 ◽

1990 ◽

Vol 48 (2) ◽

pp. 4-5

Author(s):

Ryuichi Shimizu ◽

Ze-Jun Ding

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Angular Distribution ◽

Elastic Scattering ◽

Electron Scattering ◽

Cross Sections ◽

Expansion Method ◽

Electron Excitation ◽

Partial Wave Expansion ◽

Backscattered Electrons

Monte Carlo simulation has been becoming most powerful tool to describe the electron scattering in solids, leading to more comprehensive understanding of the complicated mechanism of generation of various types of signals for microbeam analysis.The present paper proposes a practical model for the Monte Carlo simulation of scattering processes of a penetrating electron and the generation of the slow secondaries in solids. The model is based on the combined use of Gryzinski’s inner-shell electron excitation function and the dielectric function for taking into account the valence electron contribution in inelastic scattering processes, while the cross-sections derived by partial wave expansion method are used for describing elastic scattering processes. An improvement of the use of this elastic scattering cross-section can be seen in the success to describe the anisotropy of angular distribution of elastically backscattered electrons from Au in low energy region, shown in Fig.l. Fig.l(a) shows the elastic cross-sections of 600 eV electron for single Au-atom, clearly indicating that the angular distribution is no more smooth as expected from Rutherford scattering formula, but has the socalled lobes appearing at the large scattering angle.

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Determination of Stoichiometry of GaAs Component in (Gaas)Ge Epitaxially Grown Thin Films by Electron Microprobe X-Ray Analysis

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100134922 ◽

1990 ◽

Vol 48 (2) ◽

pp. 264-265

Author(s):

D. R. Liu ◽

S. S. Shinozaki ◽

R. J. Baird

Keyword(s):

Thin Film ◽

Thin Films ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Compound Semiconductors ◽

Energy Dispersive Analysis ◽

X Ray ◽

Metastable Alloys ◽

Lower Voltage

The epitaxially grown (GaAs)Ge thin film has been arousing much interest because it is one of metastable alloys of III-V compound semiconductors with germanium and a possible candidate in optoelectronic applications. It is important to be able to accurately determine the composition of the film, particularly whether or not the GaAs component is in stoichiometry, but x-ray energy dispersive analysis (EDS) cannot meet this need. The thickness of the film is usually about 0.5-1.5 μm. If Kα peaks are used for quantification, the accelerating voltage must be more than 10 kV in order for these peaks to be excited. Under this voltage, the generation depth of x-ray photons approaches 1 μm, as evidenced by a Monte Carlo simulation and actual x-ray intensity measurement as discussed below. If a lower voltage is used to reduce the generation depth, their L peaks have to be used. But these L peaks actually are merged as one big hump simply because the atomic numbers of these three elements are relatively small and close together, and the EDS energy resolution is limited.

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