The generalized modified Bessel function and its connection with Voigt line profile and Humbert functions

Kumar, Rahul

doi:10.1016/j.aam.2019.101986

Cited by 13 publications

(6 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where σ is the length scale, ν controls the smoothness of the resulting function, Γ f (.) is the gamma function, and K ν is a modified Bessel function [19].…”

Section: Gaussian Process Regressor (Gpr)mentioning

confidence: 99%

Gaussian processes regression for cyclodextrin host-guest binding prediction

Carvalho

Rosa

Gomes

et al. 2021

J Incl Phenom Macrocycl Chem

View full text Add to dashboard Cite

Machine Learning (ML) techniques are becoming an integral part of rational drug design and discovery. Data-driven modeling regularly outperforms physics-based models for predicting molecular binding affinities, placing ML as a promising tool. Cyclodextrins are nano-cages used to improve the delivery of insoluble or toxic drugs. Due to chemical similarity to proteins, ML approaches could vastly profit to improve affinity prediction and enhance their carriable drug portfolio. Here we evaluate the performance of three well-known ML methods -Support Vector Regression (SVR), Gaussian Process Regression (GPR), and eXtreme Gradient Boosting (XGB) -to predict the binding affinity of cyclodextrin and known ligands. We perform hyperparameter tuning through Random Search. The results were compatible with the presented literature. We were able to increase our previous prediction performance and present a GPR model to adjust to the data (R 2 = 0.803) with low prediction errors (RMSE = 1.811 kJ/mol and MAE = 1.201 kJ/mol).

show abstract

“…where σ is the length scale, ν controls the smoothness of the resulting function, Γ f (.) is the gamma function, and K ν is a modified Bessel function [19].…”

Section: Gaussian Process Regressor (Gpr)mentioning

confidence: 99%

Gaussian processes regression for cyclodextrin host-guest binding prediction

Carvalho

Rosa

Gomes

et al. 2021

J Incl Phenom Macrocycl Chem

View full text Add to dashboard Cite

show abstract

“…where ν and l are positive parameters, (•) is the Gamma function and K ν (•) is the modified Bessel function (Kumar, 2020). The building of the GPR model involves the proper setting of the kernel parameters k 0 , ν, and l and the regularization coefficient α.…”

Section: Gaussian Processes Regressionmentioning

confidence: 99%

Estimation of natural streams longitudinal dispersion coefficient using hybrid evolutionary machine learning model

Goliatt

Sulaiman

Khedher

et al. 2021

Engineering Applications of Computational Fluid Mechanics

View full text Add to dashboard Cite

Among several indicators for river engineering sustainability, the longitudinal dispersion coefficient (K x ) is the main parameter that defines the transport of pollutants in natural streams. Accurate estimation of K x has been challenging for hydrologists due to the high stochasticity and non-linearity of this hydraulic-environmental parameter. This study presents a new hybrid machine learning (ML) model integrating a Gaussian Process Regression (GPR) and an evolutionary feature selection (FS) approach (i.e. Covariance Matrix Adaptation Evolution Strategy (CMAES)) to estimate K x in natural streams. The dataset consists of geometric and hydraulic river system parameters from 29 streams in the United States. The modeling results showed that the proposed model outperformed other models in the literature, producing more stable and accurate estimations. The FS approach evidenced the significance of the cross-sectional average flow velocity (U), channel width (B), and channel sinuosity σ to estimate the dispersion coefficient. In quantitative terms, the integrated GPR model with feature selection approach attained the minimum root mean square error (RMSE = 48.67) and maximum coefficient of determination (R 2 = 0.95). The proposed hybrid evolutionary ML model arises as robust, flexible and reliable alternative computer aid technology for predicting the longitudinal dispersion coefficient in natural streams.

show abstract

“…There are many approximations are available in scientific literature [15][16][17][18][19][20][21][22][23][24][26][27][28]. Although this problem is known for many decades, derivation of new approximations for the complex error function w(z) and developing their efficient algorithms still remains an interesting topic [29][30][31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

A Two-Domain MATLAB Implementation for Efficient Computation of the Voigt/Complex Error Function

Abrarov¹,

Siddiqui²,

Jagpal³

et al. 2022

Preprint

View full text Add to dashboard Cite

In this work we develop a new algorithm for efficient computation of the Voigt/complex error function. In particular, in this approach we propose a two-domain scheme where number of the grid-points is dependent on the input parameter $y$. The error analysis we performed shows that the MATLAB implementation meets the requirements for radiative transfer applications involving the HITRAN spectroscopic database. The run-time test shows that this MATLAB implementation provides rapid computation especially at smaller range of the parameter x.

show abstract

The generalized modified Bessel function and its connection with Voigt line profile and Humbert functions

Cited by 13 publications

References 17 publications

Gaussian processes regression for cyclodextrin host-guest binding prediction

Gaussian processes regression for cyclodextrin host-guest binding prediction

Estimation of natural streams longitudinal dispersion coefficient using hybrid evolutionary machine learning model

A Two-Domain MATLAB Implementation for Efficient Computation of the Voigt/Complex Error Function

Contact Info

Product

Resources

About