2.S997 Artificial Intelligence and Machine Learning for Engineering Design - Final Presentations

Students in Course 2.S997, Artificial Intelligence and Machine Learning for Engineering Design, learn to apply AI and ML methods to design new products of systems and solve complex engineering problems. 

Join us for an in-person poster session as 15 student groups present their research. 

• Learning Deformable Point Cloud Correspondences in Medical Ultrasound Data
• Motorsports Cooling Optimization
• Optimized Torsion Spring for Rehabilitative Exoskeleton
• Nozzle Constraints Aware Data-driven Topology Optimization
• Predicting Loads on a Decentralized Grid
• Wind Farm Model and Wake Steering Optimization with PyWake
• Using Machine Learning to Optimize Black Box Simulations
• Latent Space Design Exploration of Complex Structures
• Temperature and Density Measurements using Optical Spectroscopy and Machine Learning in Inhomogenous Non-optically Thin Plasmas
• Implementing Natural language Processing to Assess the Design and Performance of the iPhone SE
• Machine learning for Inverse Kinematics
• Classifying the Impact Performance of Truss-Based Lattices
• Efficient Meshing Scheme for the AMORE Mesh
• Data-Driven Modeling of McKibben Actuator Dynamics
• Bike Design Analysis and Classification using AutoML