Algorithmic description of the plasticity model, based on the algorithm proposed in [30]. حسب Younes Bouhadjar (12902755)

الموضوعات:

Hierarchical relationship of sequence contexts and key algorithmic elements of Baymer. حسب Christopher J. Adams (1847317)

Analysis of dictionary components learned by the SSMCA algorithm on natural image patches. حسب Jacquelyn A. Shelton (2210767)

Depiction of the elements of our reservoir computing model. حسب Joseph Chrol-Cannon (596144)

الموضوعات:

Flowchart of the DQN algorithm for the SC optimal design. حسب Chang Soon Kim (17480874)

, MSE, and MAE of the optimised algorithms. حسب Itxasne Antúnez Sáenz (22794696)

Range of the variables in the finite element simulations which were used to train and test the machine learning algorithms. حسب Jenson Jacob (17468087)

الموضوعات:

Design parameters of the FEM-DQN algorithm for the SC design. حسب Chang Soon Kim (17480874)

ML algorithms in cyber security. حسب Abdullah Asım Yılmaz (20715311)

Concept and design process of the FEM-DQN algorithm for the SC optimal design. حسب Chang Soon Kim (17480874)

Algorithmic experimental parameter design. حسب Chuanxi Xing (20141665)

"…Finally, we derive an off-grid sparse model for the reconstructed signal by exploiting sparsity in the null domain and use Bayesian learning to compute the maximum a posteriori probability of the source signal, thus achieving DOA estimation. …"

Algorithms used in this study. حسب Daniel Sanchez-Gomez (19065975)

"…This allowed us to develop a machine learning-based framework for the prediction of bead-forming minerals by training and benchmarking 13 of the most widely used supervised algorithms. …"

Spatial spectrum estimation for three algorithms. حسب Chuanxi Xing (20141665)

"…Finally, we derive an off-grid sparse model for the reconstructed signal by exploiting sparsity in the null domain and use Bayesian learning to compute the maximum a posteriori probability of the source signal, thus achieving DOA estimation. …"

Prediction performance for a sequence set with 10 overlapping elements. حسب Younes Bouhadjar (12902755)

الموضوعات:

Data_Sheet_1_Modeling Macroscopic Material Behavior With Machine Learning Algorithms Trained by Micromechanical Simulations.CSV حسب Denise Reimann (7213364)

"…In this work, we suggest a new approach to develop microstructure-sensitive, yet flexible and numerically efficient macroscopic material models by using micromechanical simulations for training Machine Learning (ML) algorithms to capture the mechanical response of various microstructures under different loads. …"

Data_Sheet_2_Modeling Macroscopic Material Behavior With Machine Learning Algorithms Trained by Micromechanical Simulations.pdf حسب Denise Reimann (7213364)

"…In this work, we suggest a new approach to develop microstructure-sensitive, yet flexible and numerically efficient macroscopic material models by using micromechanical simulations for training Machine Learning (ML) algorithms to capture the mechanical response of various microstructures under different loads. …"

The workflow of the developed modelling and Machine Learning (ML) architectural structure. حسب Jenson Jacob (17468087)

الموضوعات:

Flowchart of the subspace fitting algorithm for interpolated coprime arrays. حسب Chuanxi Xing (20141665)

Risk element category diagram. حسب Yao Hu (3479972)

Description of the network model. حسب Younes Bouhadjar (12902755)

الموضوعات: