Code program. by Honglei Pang (22693724)

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

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Steering system model. by Honglei Pang (22693724)

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

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

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Co-simulation architecture. by Honglei Pang (22693724)

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

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Braking system model. by Honglei Pang (22693724)

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Vehicle parameters. by Honglei Pang (22693724)

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Locally sparse function-on-function regression by Mauro Bernardi (418110)

“…Herein, we consider the case where both the response and covariates are functions. …”

A comparison between the static Python-based visualizations of the p65 activity in activated fibroblasts and the dynamic, HTML-based visualizations that use these same reduction me... by Hector Torres (11708207)

On the improvement of reinforcement active learning with the involvement of cross entropy to address one-shot learning problem by Honglan Huang (341951)

“…With the involvement of cross-entropy in the loss function of Q-learning, an efficient policy to decide when and where to predict or query an instance is learned through the developed framework. …”

Task structure diagram. by Honglan Huang (341951)

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

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

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

“trend+var” algorithm. by Roman Olson (6577004)

State Preparation in Quantum Algorithms for Fragment-Based Quantum Chemistry by Ruhee D’Cunha (8921372)

“…The localized active space–unitary coupled cluster (LAS–UCC) algorithm iteratively loads a fragment-based multireference wave function onto a quantum computer. …”

Physics-Informed Bayesian Optimization for Conformational Ensemble Augmentation by Ivan A. Bespalov (21491561)

“…In this paper, we introduce a Bayesian optimization algorithm for conformational ensemble augmentation, that is, locating missing conformers in an existing ensemble, which employs Bayesian optimization with physics-informed torsion-potential-based kernel function and a novel acquisition function that prioritizes potential energy surface exploration for increased conformer diversity. …”

Physics-Informed Bayesian Optimization for Conformational Ensemble Augmentation by Ivan A. Bespalov (21491561)

“…In this paper, we introduce a Bayesian optimization algorithm for conformational ensemble augmentation, that is, locating missing conformers in an existing ensemble, which employs Bayesian optimization with physics-informed torsion-potential-based kernel function and a novel acquisition function that prioritizes potential energy surface exploration for increased conformer diversity. …”