Papers coauthored with Jason Geder
Publications
Insights into Flexible Bioinspired Fins for Unmanned Underwater Vehicle Systems through Deep Learning
We introduce forward neural network models that integrate fin stiffness to predict UUV kinematics, thrust, and efficiency, improving performance in bio-inspired fin designs.
Insights into Flexible Bioinspired Fins for Unmanned Underwater Vehicle Systems through Deep Learning
We introduce forward neural network models that integrate fin stiffness to predict UUV kinematics, thrust, and efficiency, improving performance in bio-inspired fin designs.
Power-Aware Inverse-Search Machine Learning for Low Resource Multi-Objective Unmanned Underwater Vehicle Control
Developed power-aware machine learning models for underewater vehicle control, improving efficiency while maintaining performance.
Data-Driven Machine Learning Models for a Multi-Objective Flapping Fin Unmanned Underwater Vehicle Control System
We develop a search-based inverse model that leverages a kinematics-to-thrust neural network to determine flapping fin kinematics achieving target thrust and smooth transitions, enabling real-time control adjustments for UUV propulsion.
Computational Approaches for Modeling Power Consumption on an Underwater Flapping Fin Propulsion System
We develop a non-dimensional figure of merit (FOM) to evaluate fin designs and kinematics for bio-inspired UUVs, using computational models to predict thrust, power, and efficiency, optimizing gait performance and material selection.