SIAM Student Chapter Seminar

• When: Fridays at 1 PM unless noted otherwise
• Where: 9th floor lounge (we will also broadcast the virtual talks on the 9th floor lounge with refreshments)
• Organizers: Yahui Qu, Peiyi Chen and Zaidan Wu
• Faculty advisers: Jean-Luc Thiffeault, Steve Wright
• To join the SIAM Chapter mailing list: email siam-chapter+join@g-groups.wisc.edu.
• Zoom link: https://uwmadison.zoom.us/j/97976615799?pwd=U2xFSERIcnR6M1Y1czRmTjQ1bTFJQT09
• Passcode: 281031

Fall 2024

Date	Location	Speaker	Title
10 AM 10/4	Birge 346	Federica Ferrarese (University of Ferrara, Italy)	Control plasma instabilities via an external magnetic field: deterministic and uncertain approaches
10/18	9th floor	Martin Guerra (UW-Madison)	Swarm-Based Gradient Descent Meets Simulated Annealing
11/1
11/8	9th floor	Borong Zhang (UW-Madison)

Abstracts

October 4th, Federica Ferrarese (University of Ferrara, Italy): The study of the problem of plasma confinement in huge devices, such as for example Tokamaks and Stellarators, has attracted a lot of attention in recent years. Strong magnetic fields in these systems can lead to instabilities, resulting in vortex formation. Due to the extremely high temperatures in plasma fusion, physical materials cannot be used for confinement, necessitating the use of external magnetic fields to control plasma density. This approach involves studying the evolution of plasma, made up of numerous particles, using the Vlasov-Poisson equations. In the first part of the talk, the case without uncertainty is explored. Particle dynamics are simulated using the Particle-in-Cell (PIC) method, known for its ability to capture kinetic effects and self-consistent interactions. The goal is to derive an instantaneous feedback control that forces the plasma density to achieve a desired distribution. Various numerical experiments are presented to validate the results. In the second part, uncertainty is introduced into the system, leading to the development of a different control strategy. This method is designed to steer the plasma towards a desired configuration even in the presence of uncertainty. The presentation concludes with a comparison of the two control strategies, supported by various numerical experiments.

October 18th, Martin Guerra (UW-Madison): In generic non-convex optimization, one needs to be able to pull samples out of local optimal points to achieve global optimization. Two common strategies are deployed: adding stochasticity to samples such as Brownian motion, as is done in simulated annealing (SA), and employing a swarm of samples to explore the whole landscape, as is done in Swarm-Based Gradient Descent (SBGD). The two strategies have severe drawbacks but complement each other on their strengths. SA fails in the accuracy sense, i.e., finding the exact optimal point, but succeeds in always being able to get close, while SBGD fails in the probability sense, i.e., it has non-trivial probability to fail, but if succeeds, can find the exact optimal point. We propose to combine the strength of the two and develop a swarm-based stochastic gradient method with samples automatically adjusting their annealing. Using mean-field analysis and long-time behavior PDE tools, we can prove the method to succeed in both the accuracy sense and the probability sense. Numerical examples verify these theoretical findings.

Past Semesters

• Spring 2024
• Fall 2023
• Spring 2023
• Fall 2022
• Spring 2022
• Fall 2021
• Fall 2020
• Spring 2020
• Fall 2019
• Fall 2018
• Spring 2017