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

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

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

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

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

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

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

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

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

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

Algorithmic experimental parameter design. by 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. by 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. by 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. by Younes Bouhadjar (12902755)

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Data_Sheet_1_Modeling Macroscopic Material Behavior With Machine Learning Algorithms Trained by Micromechanical Simulations.CSV by 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 by 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. by Jenson Jacob (17468087)

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

Risk element category diagram. by Yao Hu (3479972)

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

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