One advantage of our algorithm is that it is not binary in nature. by Yong Kuk Kim (13955214)

Thesis-RAMIS-Figs_Slides by Felipe Santibañez-Leal (10967991)

“…<br><br>Finally, although the developed concepts, ideas and algorithms have been developed for inverse problems in geostatistics, the results are applicable to a wide range of disciplines where similar sampling problems need to be faced, included but not limited to design of communication networks, optimal integration and communication of swarms of robots and drones, remote sensing.…”

Latin sampling. by Honghua Liu (15307022)

AUW-CE Mining Algorithms & Dataset Hub by Shuaikang Yuan (21091847)

“…Moreover, in response to the limitations of conventional cross-entropy methods for HUCPM, four core optimization strategies are designed: optimization of the initial probability distribution to guide the search direction, enhancement of sample diversity to prevent local convergence, dynamic adjustment of sample size to reduce redundant calculations, and incorporation of utility weights to improve the accuracy of probability updates. …”

NucB sampling: by Carlos A. Gomez-Uribe (21492788)

Alpha amylase sampling: by Carlos A. Gomez-Uribe (21492788)

PANet network design. by Mengqi Yuan (4852555)

“…Second, a dynamic up-sampling technique was introduced to improve the model’s ability to recover fine details. …”

BiFPN network design. by Mengqi Yuan (4852555)

“…Second, a dynamic up-sampling technique was introduced to improve the model’s ability to recover fine details. …”

MCLP_quantum_annealer_V0.5 by Anonymous Anonymous (4854526)

Accelerated Design for High-Entropy Alloys Based on Machine Learning and Multiobjective Optimization by Yingying Ma (3617810)

“…Notably, the <i>D</i> of three candidates have shown significant improvements compared to the samples with similar <i>H</i> in the original data sets, with increases of 135.8, 282.4, and 194.1% respectively. …”

Accelerated Design for High-Entropy Alloys Based on Machine Learning and Multiobjective Optimization by Yingying Ma (3617810)

“…Notably, the <i>D</i> of three candidates have shown significant improvements compared to the samples with similar <i>H</i> in the original data sets, with increases of 135.8, 282.4, and 194.1% respectively. …”

Performance of the optimal classifiers on each feature list. by Wenwei Wu (9352541)

Data_Sheet_1_Critical Evaluation of Metaheuristic Algorithms for Weight Minimization of Truss Structures.docx by Aristotelis E. Charalampakis (7454258)

Three sensor sample data with noise data. by Tang Ruipeng (20141334)

Third setting: batch optimization. by Camille Gontier (9442304)

Policy network (<i>θ</i>′) of SAM to accommodate PPO algorithm. by Zhenhan Huang (12107002)

Code and data for Lambert and Ellner, "SDM meets eDNA: optimal sampling of environmental DNA to estimate species-environment relationships in stream networks", Ecography (2025) by Timothy Lambert (16890087)

“…The code includes: (1) an iterative generalized least squares solution method for estimating model parameters, (2) a genetic algorithm for finding D-optimal sampling designs (i.e., the positioning of samples on a stream network that most accurately estimates species-environment relationships), and (3) generalized additive models for estimating the dependence of estimation accuracy on a stream network's topological and hydrologic properties.…”

Upset graph to show the intersection of features used in three optimal classifiers. by Wenwei Wu (9352541)

Algorithm parameter settings. by Yue Guo (695495)

Study design flow chart. by Müge Yalçin (16486563)