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1	Locating Diversity in Reservoir Computing Using Bayesian Hyperparameter Optimization Lunceford, Whitney 06 September 2024 (has links) (PDF) Reservoir computers rely on an internal network to predict the future state(s) of dynamical processes. To understand how a reservoir's accuracy depends on this network, we study how varying the networ's topology and scaling affects the reservoir's ability to predict the chaotic dynamics on the Lorenz attractor. We define a metric for diversity, the property describing the variety of the responses of the nodes that make up reservoir's internal network. We use Bayesian hyperparameter optimization to find optimal hyperparameters and explore the relationships between diversity, accuracy of model predictions, and model hyperparameters. The content regarding the VPT metric, the effects of network thinning on reservoir computing, and the results from grid search experiments mentioned in this thesis has been done previously. The results regarding the diversity metric, kernel tests, and results from BHO are new and use this previous work as a comparison to the quality and usefulness of these new methods in creating accurate reservoir computers. reservoir computing Bayesian hyperparameter optimization diversity thinning attractor reconstruction Physical Sciences and Mathematics
2	Wildfire Spread Prediction Using Attention Mechanisms In U-Net Shah, Kamen Haresh, Shah, Kamen Haresh 01 December 2022 (has links) (PDF) An investigation into using attention mechanisms for better feature extraction in wildfire spread prediction models. This research examines the U-net architecture to achieve image segmentation, a process that partitions images by classifying pixels into one of two classes. The deep learning models explored in this research integrate modern deep learning architectures, and techniques used to optimize them. The models are trained on 12 distinct observational variables derived from the Google Earth Engine catalog. Evaluation is conducted with accuracy, Dice coefficient score, ROC-AUC, and F1-score. This research concludes that when augmenting U-net with attention mechanisms, the attention component improves feature suppression and recognition, improving overall performance. Furthermore, employing ensemble modeling reduces bias and variation, leading to more consistent and accurate predictions. When inferencing on wildfire propagation at 30-minute intervals, the architecture presented in this research achieved a ROC-AUC score of 86.2% and an accuracy of 82.1%. Image Segmentation Wildfire Spread Prediction Google Earth Engine Satellite Images Deep Learning U-Net Attention Ensemble Modeling Bayesian Hyperparameter Optimization Threshold Tuning TensorFlow Python Wildfires Artificial Intelligence and Robotics Computational Engineering Software Engineering

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Locating Diversity in Reservoir Computing Using Bayesian Hyperparameter Optimization

Wildfire Spread Prediction Using Attention Mechanisms In U-Net