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)

Visualization of the RITSS algorithm. by Julian Hecker (758791)

The target gravitational function and the repulsion function are added. by Zhenggang Wang (1753657)

Subjects: “…target gravitational function…”

Available functions in the phase-shifting package. by Raul Castañeda (13932971)

Available functions in the numerical propagation package. by Raul Castañeda (13932971)

Available functions in the phase reconstruction package. by Raul Castañeda (13932971)

Comparison of algorithm search curves. by Bowen Li (200859)

“…Then, combining with the idea of fast density clustering algorithm, the number of hidden layer neurons of RBF is determined by finding the point with the highest density and using it as the hidden layer neuron. …”

Summary of classical and CEC-based benchmark test functions used in this study. by Mahmood A. Jumaah (22272371)

Subjects:

Verification of the in-line PS function. by Raul Castañeda (13932971)

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

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

“…<p dir="ltr"><b>Opti2Phase: Python Scripts for Two-Stage Focal Reducer Design</b></p><p dir="ltr">The folder <b>Opti2Phase</b> contains the Python scripts used to generate the results presented in the manuscript. …”

Penalty functions used in different methods. by Souvik Seal (12437166)

Generating the PSC kernel using a double exponential function. by Alireza Ghadimi (13839874)

Algorithm parameters. by Ruyi Dong (9038174)

Subjects: “…effective optimization algorithm…”

Limitation of the conventional DBT reconstruction algorithm. by Yunsu Choi (11993484)

Schematic diagram of dynamic step size expansion. by Zhenggang Wang (1753657)

Subjects: “…target gravitational function…”

Comparison of step size 0.1 with the actual energy consumption step count. by Zhiqun Lin (1429501)

Subjects:

Flowchart of the specific incarnation of the BO algorithm used in the experiments. by Lisa Laux (9367681)

“…To choose the next pipeline configuration to evaluate, the BO algorithm uses an Expected Improvement function to trade off maximisation of QS with the need to fully learn the GP. …”

Images of partial benchmark functions. by ZeSheng Lin (20501356)

Subjects:

The real NGD values, one-step ahead training set predictions, and multi-step ahead test set predictions using the AI-AFTER algorithm for Kayseri. by Burak Alakent (463512)