Maximizing Number of Protons in Fusion Process Using Machine Learning
Speaker Bio: Ethan Rodriguez is a student at St. Mary’s University, pursuing both undergraduate and master’s courses in software engineering, along with a minor in physics. Now entering his third year of undergraduate studies, Ethan recently completed a summer internship at the Lawrence Berkeley National Laboratory, where he worked in the Computational Research Division. During this internship, he contributed to a project focused on enhancing the fusion ignition process through machine learning techniques. This experience not only aligned with Ethan’s interest in physics but also deepened his understanding of machine learning.
Abstract: To enhance the efficiency of fusion ignition processes by maximizing proton production, a neural network (NN) has been trained on experimental data from the BELLA iP2 laser facility. The NN parameters have been optimized to fit this data. The next step involves training the NN using both experimental and simulation data, while maintaining their correlation, to eliminate the need for a time-consuming experimental campaign across all input parameters. This NN will help identify the optimal operating parameters to maximize proton output within a specified energy range.
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- Date: 07 Oct 2024
- Time: 07:30 PM to 08:30 PM
- All times are (UTC-05:00) Central Time (US & Canada)
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- One Camino Santa Maria
- St. Mary's University
- San Antonio, Texas
- United States 78228
- Building: University Center
- Room Number: Alumni Conference Room
- Click here for Map
- Starts 21 September 2024 12:00 AM
- Ends 07 October 2024 07:25 PM
- All times are (UTC-05:00) Central Time (US & Canada)
- No Admission Charge