AI-driven Uncertainty Reduction in Electromagnetic Forming (EMF) Using LS-DYNA and HPC Deep Learning

• Analyzed electromagnetic forming rebound behavior and optimized process conditions via LS-DYNA simulations (Manuscript in Preparation)
• Trained TFT deep-learning models to predict form depth and current amplitude from limited experimental data
• Implemented Monte Carlo Dropout to quantify uncertainty, guiding synthetic data creation to improve model accuracy
• Represented Cooper Union as official delegate and received full sponsorship for travel, living, and research expenses