VIRIAL COEFFICIENTS FOR THE HARD GAUSSIAN OVERLAP MODEL

1999 ◽  
Vol 10 (02n03) ◽  
pp. 361-374 ◽  
Author(s):  
SSU-LI HUANG ◽  
VENKAT R. BHETHANABOTLA
Keyword(s):  
Monte Carlo ◽  
Aspect Ratio ◽  
Excellent Agreement ◽  
Virial Coefficients ◽  
Helmholtz Energy ◽  
Analytical Functions ◽  
Analytical Integration ◽  
Hgo Model ◽  
Overlap Model ◽  
Better Than

Monte Carlo estimates of virial coefficients up to the sixth for the Hard Gaussian Overlap (HGO) model are presented for values of the aspect ratio parameter κ of the model ranging from 0.05 to 10. The sixth coefficients are new and the lower coefficients are improvements on previous numerical estimates. The second virials are found to be in excellent agreement with an analytical integration reported in the literature. Padé (3, 3) approximations to the pressure and residual Helmholtz energy were constructed. Attempts to represent coefficients in these approximations by analytical functions of κ were not successful due to singularities in these functions. In the approximate range of 4.5≤κ≤ 5.5, the (3, 3) Padé approximations were found to be no better than lower ones. Comparisons with available Monte Carlo simulated pressures for moderately aspherical fluids were found to be good.

Formation of GaN Self-Organized Nanotips by Nanomasking Effect

2001 ◽  
Vol 707 ◽  
Author(s):  
Harumasa Yoshida ◽  
Tatsuhiro Urushido ◽  
Hideto Miyake ◽  
Kazumasa Hiramtsu
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Aspect Ratio ◽  
Formation Mechanism ◽  
High Aspect Ratio ◽  
Photoelectron Spectroscopy ◽  
Nanometer Scale ◽  
Xps Analysis ◽  
X Ray ◽  
Self Organized

ABSTRACTWe have successfully fabricated self-organized GaN nanotips by reactive ion etching using chlorine plasma, and have revealed the formation mechanism. Nanotips with a high density and a high aspect ratio have been formed after the etching. We deduce from X-ray photoelectron spectroscopy (XPS) analysis that the nanotip formation is attributed to nanometer-scale masks of SiO2 on GaN. The structures calculated by Monte Carlo simulation of our formation mechanism are very similar to the experimental nanotip structures.

Adaptive Sampling Based on GH-Distance for Realistic Image Synthesis

2014 ◽  
Vol 998-999 ◽  
pp. 806-813
Author(s):  
Jian Wang ◽  
Qing Xu
Keyword(s):  
Monte Carlo ◽  
Computer Graphics ◽  
Adaptive Sampling ◽  
Random Noise ◽  
Image Synthesis ◽  
Simulation Methods ◽  
Path Tracing ◽  
Complex Scenes ◽  
Realistic Image ◽  
Better Than

Realistic image synthesis technology is an important part in computer graphics. Monte Carlo based light simulation methods, such as Monte Carlo path tracing, can deal with complex lighting computations for complex scenes, in the field of realistic image synthesis. Unfortunately, if the samples taken for each pixel are not enough, the generated images have a lot of random noise. Adaptive sampling is attractive to reduce image noise. This paper proposes a new GH-distance based adaptive sampling algorithm. Experimental results show that the method can perform better than other similar ones.

Asteroid masses obtained with INPOP planetary ephemerides

2019 ◽  
Vol 492 (1) ◽  
pp. 589-602 ◽  
Author(s):  
A Fienga ◽  
C Avdellidou ◽  
J Hanuš
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Sampling ◽  
New Method ◽  
Navigation Data ◽  
The Earth ◽  
Better Than ◽  
Juno Mission

ABSTRACT In this paper, we present masses of 103 asteroids deduced from their perturbations on the orbits of the inner planets, in particular Mars and the Earth. These determinations and the INPOP19a planetary ephemerides are improved by the recent Mars orbiter navigation data and the updated orbit of Jupiter based on the Juno mission data. More realistic mass estimates are computed by a new method based on random Monte Carlo sampling that uses up-to-date knowledge of asteroid bulk densities. We provide masses with uncertainties better than 33${{\ \rm per\ cent}}$ for 103 asteroids. Deduced bulk densities are consistent with those observed within the main spectroscopic complexes.

scholarly journals A graph-based genetic algorithm and generative model/Monte Carlo tree search for the exploration of chemical space

2019 ◽  
Vol 10 (12) ◽  
pp. 3567-3572 ◽  
Author(s):  
Jan H. Jensen
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Monte Carlo ◽  
Chemical Space ◽  
Generative Model ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Synthetic Accessibility ◽  
Better Than

This paper presents a comparison of a graph-based genetic algorithm (GB-GA) and machine learning (ML) results for the optimization of log P values with a constraint for synthetic accessibility and shows that the GA is as good as or better than the ML approaches for this particular property.

The magnetic flux and self-inductivity of a thick toroidal current

2007 ◽  
Vol 73 (5) ◽  
pp. 741-756 ◽  
Author(s):  
TOMISLAV ŽIC ◽  
BOJAN VRŠNAK ◽  
MARINA SKENDER
Keyword(s):  
Aspect Ratio ◽  
Magnetic Flux ◽  
Magnetic Energy ◽  
The Other ◽  
Poloidal Field ◽  
Other Hand ◽  
Radial Profiles ◽  
Internal Flux ◽  
The Difference ◽  
Better Than

AbstractWe investigate numerically the magnetic flux and self-inductivity of a toroidal current I of arbitrary aspect ratio (R0/r0 = 1/η, where R0 and r0 are the major and the minor torus radii, respectively). The total flux Ψ is represented by the sum of the flux outside the torus envelope (Ψo) and the internal flux within the torus body (Ψi). Analogously, the total inductivity is expressed as L = Lo + Li. The outside self-inductivity is determined directly from the magnetic flux Ψo, utilizing Ψo = LoI. On the other hand, the internal inductivity is evaluated as the magnetic energy contained in the poloidal field. The calculations are performed for three different radial profiles of the current density, j(r).It is found that Ψo(η) and Lo (η) depend only very weakly on the form of j(r). On the other hand, Ψi and Li do not depend on η, but depend on the form of j(r). In the range 0.02 ≲ η ≲ 0.5, the numerical values of Lo can be very well fitted by the function of the form Lofit1(η) = −A log(η) − B. Such a relation is analogous to that for a slender torus, although the coefficients are different. For η ≲ 0.01 the slender-torus approximation (Lo*) matches the numerical results better than our function Lofit1, whereas for thicker tori, Lofit1 becomes more appropriate. It is shown that, beyond η ≳ 0.1, the departure of the slender-torus analytical expression from the numerical values becomes greater than 10%, and the difference becomes larger than 100% at η 0.55. In the range η 0.5, the numerical values of Lo can be very well expressed by the function Lofit2(η)=c1 (1 − η)c2. Furthermore, since the internal flux and inductivity become larger than that outside the envelope, Ψi and Li become larger than Ψo and Lo. The total inductivity Ltotfit = Lofit + Li, calculated by appropriately employing our functions Lofit1 and Lofit2, never deviates by more than 1% from the numerically determined values of Ltot.

Mayer-sampling Monte Carlo calculations of uniquely flexible contributions to virial coefficients

2011 ◽  
Vol 135 (12) ◽  
pp. 124101 ◽  
Author(s):  
Katherine R. S. Shaul ◽  
Andrew J. Schultz ◽  
David A. Kofke
Keyword(s):  
Monte Carlo ◽  
Virial Coefficients ◽  
Monte Carlo Calculations

Performance of Msplit estimates in the context of vertical displacement analysis

2020 ◽  
Vol 14 (2) ◽  
pp. 149-158 ◽  
Author(s):  
Patrycja Wyszkowska ◽  
Robert Duchnowski
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Least Squares ◽  
Vertical Displacement ◽  
Least Squares Estimation ◽  
Small Magnitude ◽  
Displacement Analysis ◽  
Better Than

AbstractThis paper concerns two types of Msplit estimation: squared Msplit estimation (SMS), which assumes normality of observation errors and absolute Msplit estimation (AMS), which applies {\text{L}_{1}} norm criterion. The main objective of the paper is to assess the accuracy of such estimators in vertical displacement analysis by applying Monte Carlo simulations. Another issue is to compare the accuracy of both estimators with the accuracy of the least squares estimation (LS). The paper shows that the accuracy of both Msplit estimates is like the accuracy of LS estimates. However, if some nonrandom errors occur, then accuracy of AMS estimates might be better than the accuracy of the rest of the estimates considered here. It stems from the fact that AMS estimates are robust against disturbances which have a small magnitude. It is also worth noting that the accuracy of both Msplit estimates might depend on the magnitude of the displacement.

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