A Genetic Algorithm Approach for Compact Wave Function Representations in Spin-Adapted Bases by Maru Song (22593561)

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Gillespie algorithm simulation parameters. by Nicholas H. Vitale (20469289)

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Summary of the contrasts obtained from the normalized images for both inclusions as a function of the host type and the wavelength. by María Victoria Waks-Serra (21578611)

Implementation algorithm. by Michael A. Colman (7163735)

EALM algorithm. by Weibo Wang (782424)

Loss function value changes in CNN training changes with epochs for both training dataset and testing dataset. by Jiaxi Wang (545353)

The image of the Aekley function. by Zhengyu Xu (8550660)

The ALO algorithm optimization flowchart. by Wenjing Wang (181404)

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The IALO algorithm solution flowchart. by Wenjing Wang (181404)

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Meta data for selected plots of "Optimal sampling of tensor networks targeting wave function’s fast decaying tails" by Marco Ballarin (17780767)

Continuous Probability Distributions generated by the PIPE Algorithm by LUIS G.B. PINHO (14073372)

“…<div><p>Abstract We investigate the use of the Probabilistic Incremental Programming Evolution (PIPE) algorithm as a tool to construct continuous cumulative distribution functions to model given data sets. …”

Overview of the algorithm used to calculate Lyapunov exponents for assessing the stability and chaotic behavior of dynamical systems. by Adil Jhangeer (9711917)

Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional by Ryan Stocks (16867476)

“…Kohn–Sham density functional theory (KS-DFT) has become a cornerstone for studying the electronic structure of molecules and materials. …”

The SSIM for the different algorithms. by Bingbing Li (461702)

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Figures and supplemental figures for "Optimal sampling of tensor networks targeting wave function’s fast decaying tails" by Marco Ballarin (17780767)

“…<p dir="ltr">Figures and supplemental figures for "Optimal sampling of tensor networks targeting wave function’s fast decaying tails". The supplemental figures are the data from the "Dibona" machine regarding the energy consumption of the algorithm. …”

Animations as supplemental material for "Optimal sampling of tensor networks targeting wave function’s fast decaying tails" by Marco Ballarin (17780767)

The pseudocode for the NAFPSO algorithm. by Huichao Guo (14515171)

“…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”

PSO algorithm flowchart. by Huichao Guo (14515171)

“…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”

Prediction performance of different optimization algorithms. by Ali-Kemal Aydin (10968731)

“…<p>(A) 3 algorithms were compared in terms of the residuals of the cost function of the optimized TF on 7 mice datasets (Derivative free algorithm failed in optimizing a TF in a mouse). …”

Standard functions to test. by Kemal Gökhan Nalbant (18858009)