BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Chicago BEGIN:DAYLIGHT DTSTART:20240310T030000 TZOFFSETFROM:-0600 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:CDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20241103T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0600 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:CST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20241008T144304Z UID:4E100B28-9CEE-40BF-9BE5-E4B9CE5019DD DTSTART;TZID=America/Chicago:20241007T193000 DTEND;TZID=America/Chicago:20241007T203000 DESCRIPTION:Speaker Bio: Ethan Rodriguez is a student at St. Mary’s Unive rsity\, pursuing both undergraduate and master’s courses in software eng ineering\, along with a minor in physics. Now entering his third year of u ndergraduate studies\, Ethan recently completed a summer internship at the Lawrence Berkeley National Laboratory\, where he worked in the Computatio nal Research Division. During this internship\, he contributed to a projec t focused on enhancing the fusion ignition process through machine learnin g techniques. This experience not only aligned with Ethan’s interest in physics but also deepened his understanding of machine learning.\n\nAbstra ct: To enhance the efficiency of fusion ignition processes by maximizing p roton production\, a neural network (NN) has been trained on experimental data from the BELLA iP2 laser facility. The NN parameters have been optimi zed to fit this data. The next step involves training the NN using both ex perimental and simulation data\, while maintaining their correlation\, to eliminate the need for a time-consuming experimental campaign across all i nput parameters. This NN will help identify the optimal operating paramete rs to maximize proton output within a specified energy range.\n\nRoom: Alu mni Conference Room\, Bldg: University Center\, One Camino Santa Maria\, S t. Mary's University\, San Antonio\, Texas\, United States\, 78228 LOCATION:Room: Alumni Conference Room\, Bldg: University Center\, One Camin o Santa Maria\, St. Mary's University\, San Antonio\, Texas\, United State s\, 78228 ORGANIZER:wluo@stmarytx.edu SEQUENCE:23 SUMMARY:Maximizing Number of Protons in Fusion Process Using Machine Learni ng URL;VALUE=URI:https://events.vtools.ieee.org/m/435356 X-ALT-DESC:Description: <br /><p><strong>Speaker Bio:</strong> Ethan Rodrig uez is a student at St. Mary&rsquo\;s University\, pursuing both undergrad uate and master&rsquo\;s courses in software engineering\, along with a mi nor in physics. Now entering his third year of undergraduate studies\, Eth an recently completed a summer internship at the Lawrence Berkeley Nationa l Laboratory\, where he worked in the Computational Research Division. Dur ing this internship\, he contributed to a project focused on enhancing the fusion ignition process through machine learning techniques. This experie nce not only aligned with Ethan&rsquo\;s interest in physics but also deep ened his understanding of machine learning.</p>\n<p><strong>Abstract:</str ong> 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 optim ized to fit this data. The next step involves training the NN using both e xperimental 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 paramet ers to maximize proton output within a specified energy range.</p>\n<p>&nb sp\;</p>\n<p>&nbsp\;</p> END:VEVENT END:VCALENDAR