The normalized acoustic resistance (blue lines) and reactance (red dotted lines) of the optimization case in Fig 7. by Xiaogang Li (299205)

Subjects:

Cross section of the metagrating and the distribution of effective homogeneous layers. by Xiaogang Li (299205)

Subjects:

Model’s measure methods. by Songsong Wang (8088293)

“…The findings indicate that the LSTM model, when integrated with the watershed-internal KG and LLM, can effectively incorporate critical elements influencing water level changes, the accuracy of the LLM-KG-LSTM model is enhanced by 3% compared to the standard LSTM model, and the LSTM series outperforms both RNN and GRU models, Our method will guide future research from the perspective of focusing on forecasting algorithms to the perspective of focusing on the relationship between multi-dimensional disaster data and algorithm parallelism.…”

<b>Codes for "Fatigue analysis of wind turbine and load reduction through wind-farm-level yaw control"</b> by Yize Wang (19535173)

“…<p dir="ltr">The farm-level yaw control optimization algorithm Codes for "Fatigue analysis of wind turbine and load reduction through wind-farm-level yaw control"</p>…”

Accuracy of different electrical and optical neural networks for various input noise levels. by Reyhane Ahmadi (14857363)

Elements: Multiparticle collision dynamics simulations of mesoscale hydrodynamic interactions in complex soft materials and environments by Michael Howard (19421473)

Data Sheet 1_Fast forward modeling and response analysis of extra-deep azimuthal resistivity measurements in complex model.docx by Pan Zhang (206408)

“…Considering the increased detection range of EDARM and the requirements for computational efficiency, this paper presents a 2.5-dimensional (2.5D) finite element method (FEM). By leveraging the symmetry of simulated signals in the spectral domain, the algorithm reduces computation time by 50%, significantly enhancing computational efficiency while preserving accuracy. …”

Computation time as a function of the sample size on the chain graph dataset. by Tomokaze Shiratori (9635271)

Subjects:

Log-likelihood as a function of the number of selected edges on the chain graph dataset (black dashed line: The true number of edges). by Tomokaze Shiratori (9635271)

Subjects:

Computation time as a function of the sample size on the random graph dataset. by Tomokaze Shiratori (9635271)

Subjects:

F1 score of edges selected through cross-validation on the chain graph dataset. by Tomokaze Shiratori (9635271)

Subjects:

Log-likelihood as a function of the number of selected edges on the random graph dataset (black dashed line: The true number of edges). by Tomokaze Shiratori (9635271)

Subjects:

Examples of ground-truth graph structures with (<i>p</i>, <i>n</i>_≠0) = (10, 10). by Tomokaze Shiratori (9635271)

Subjects:

Number of edges selected through cross-validation on the chain graph dataset. by Tomokaze Shiratori (9635271)

Subjects:

Number of edges selected through cross-validation on the random graph dataset. by Tomokaze Shiratori (9635271)

Subjects:

Graphs of the regularization terms. by Tomokaze Shiratori (9635271)

Subjects:

F1 score of a given number of selected edges on the chain graph dataset. by Tomokaze Shiratori (9635271)

Subjects:

Computation time as a function of the number of variables on the chain graph dataset. by Tomokaze Shiratori (9635271)

Subjects:

Computation time as a function of the number of variables on the random graph dataset. by Tomokaze Shiratori (9635271)

Subjects:

F1 score of a given number of selected edges on the random graph dataset. by Tomokaze Shiratori (9635271)

Subjects: