Code program. حسب Honglei Pang (22693724)

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The HIL simulation data flowchart. حسب Honglei Pang (22693724)

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Steering system model. حسب Honglei Pang (22693724)

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Hyperparameter Configurations in PPO Training. حسب Honglei Pang (22693724)

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Main parameters of steering system. حسب Honglei Pang (22693724)

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Co-simulation architecture. حسب Honglei Pang (22693724)

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Overall framework diagram of the study. حسب Honglei Pang (22693724)

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Braking system model. حسب Honglei Pang (22693724)

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Vehicle parameters. حسب Honglei Pang (22693724)

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Combining Deep Learning Neural Networks with Genetic Algorithms to Map Nanocluster Configuration Spaces with Quantum Accuracy at Low Computational Cost حسب Johnathan von der Heyde (16828390)

"…Results focus primarily on evaluating the algorithm’s performance; however, several physical insights into possible configurations for these nanoclusters naturally emerge as well, such as geometric Au surface segregation and stoichiometric Au minimization as a function of stability.…"

Green Design Evaluation of Electrical and Electronic Equipment Based on Knowledge Graph حسب Meng-Yuan Dang (17637288)

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Experiment 1a: Setting the default value for t in FastEnsemble. حسب Yasamin Tabatabaee (18095491)

"…Left: Accuracy as a function of the model mixing parameter (x-axis). …"

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

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

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

Comparison of scores obtained by our interpenetration and scoring algorithm (ISA) and ROSETTA for a subset of structures. حسب Kevin Sawade (16726527)

"…However, our algorithm was 1000 times faster than pyROSETTA (both algorithms have been parallelized on a per-structure basis using the Python package joblib [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010531#pcbi.1010531.ref069" target="_blank">69</a>]).…"

<i>De Novo</i> Design of Molecules with Low Hole Reorganization Energy Based on a Quarter-Million Molecule DFT Screen: Part 2 حسب Joshua Staker (6401297)

"…The four generative methods evaluated here are (1) Molecule Deep Q-Networks (MolDQN), which utilizes Deep-Q learning to directly optimize molecular structure graphs for desired properties instead of generating SMILES, (2) Graph-based Genetic Algorithm (GraphGA), which uses a genetic algorithm for optimization where crossovers and mutations are defined in terms of RDKit’s reaction SMILES, (3) Generative Tensorial Reinforcement Learning (GENTRL), which is a variational autoencoder (VAE) with a learned prior distribution and optimized using reinforcement learning, and (4) Monte Carlo tree search exploration of chemical space in conjunction with a recurrent neural network (RNN) decoder (ChemTS). …"

<i>OptRAM</i>: <i>In-silico</i> strain design via integrative regulatory-metabolic network modeling حسب Fangzhou Shen (5140994)

"…Moreover previous integrated approaches generally require a pre-existing regulatory network. In this study, we developed a novel strain design algorithm, named OptRAM (<b>Opt</b>imization of <b>R</b>egulatory <b>A</b>nd <b>M</b>etabolic Networks), which can identify combinatorial optimization strategies including overexpression, knockdown or knockout of both metabolic genes and transcription factors. …"

Study design. حسب Maxence Coulombe (17921106)

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Image of the ReLU function. حسب Mengqi Yuan (4852555)

"…First, a novel backbone network was designed by incorporating new convolutional layers and the Simplified Spatial Pyramid Pooling - Fast (SimSPPF) module to strengthen feature extraction. …"