Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

Subjects:

Listed python codes of our algorithm with explanations (Python 3.9 recommended). by Takayuki Niizato (162226)

Comparison of algorithms in two cases. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Flow of the NSGA-II algorithm. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Python-based Monte Carlo Simulation Tool by Yangfan Jiang (9372782)

Subjects: “…Monte Carlo simulation, Radiation transfer, Multiple scattering algorithm, Temporal correlation function…”

The optimal solution set of NYN by using different algorithms. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

The optimal solution set of HN by using different algorithms. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information. by Yuanchen Zhao (12905580)

Subjects:

<b>Opti2Phase</b>: Python scripts for two-stage focal reducer by Morgan Najera (21540776)

“…</p><p dir="ltr">The package includes:</p><ul><li>Scripts for first-order analysis, third-order modeling, optimization using a Physically Grounded Merit Function (PGMF), and RMS-based refinement.…”

Python scripts used to model CPU errors by Poisson distribution density mass function and to draw graph of Poisson events (needs Matplot, SciPy, PysimpleGUI and NumPy) by Santhosh Kumar Rajamani (13023678)

3D modeling qualities of the AS single-template models built by including DOPE in the objective function of MODELLER. by Giacomo Janson (8138517)

Model selection for machine learning algorithm based on microbial abundances. by Elio L. Herzog (11576876)

Subjects: “…applying independent algorithms…”

3D modeling qualities of the AM multiple-template models built by including DOPE in the objective function of MODELLER. by Giacomo Janson (8138517)

Parameters of the test function. by Yiqiong Gao (16698968)

“…The adaptive adjustment of the transition probability effectively balances the development and exploration abilities of the algorithm. The improved flower pollination algorithm (IFPA) outperformed six classical benchmark functions that were used to verify the superiority of the improved algorithm. …”

Average quality scores of the models of the analysis set as a function of the <i>w</i>_<i>SP</i> with which the DOPE potential has been included in the objective function... by Giacomo Janson (8138517)

Objective function value statistics. by Chao Li (145513)

Types of training algorithms. by Caixia Yan (6647072)

Reward function related parameters. by Honglei Pang (22693724)

Subjects:

An expectation-maximization algorithm for finding noninvadable stationary states. by Robert Marsland (8616483)

“…See main text and Appendix for model details and parameters. <i>(c)</i> Pseudocode for self-consistently computing <b>R</b>* and , which is identical to standard expectation-maximization algorithms employed for problems with latent variables in machine learning.…”

Model construction and validation (A) Construction of the model based on multiple machine learning algorithms. by Chengting Wu (21485612)