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Events on Wednesday, February 24th, 2021

Plasma Theory Seminar
Special Plasma Seminar
Plasma rotation: from mass separation applications to light manipulation
Time: 9:00 am
Place: Zoom Meeting https://uwmadison.zoom.us/j/97739087835
Speaker: Renaud Gueroult, Laplace, Université de Toulouse, CNRS, Toulouse, France
Abstract: Rotation phenomena in plasmas are found everywhere, from plasma technologies to laboratory experiments and all the way to astrophysics, and can lead to peculiar effects. In this talk, I will first discuss how the mass differential confinement properties found in a rotating plasma could enable developing mass separation technologies [1] with high upside potential for societal applications including nuclear waste cleanup and rare earth recycling. I will then show how the problem of crossed-field rotation control is intimately tied to the question of what cross-field conductivity is in a rotating plasma, and how answering this basic question is also crucial to certain alternative magnetic confinement fusion concepts [2]. Finally, as a further illustration of the peculiar effects of rotation, I will briefly touch on the basic effect of rotation on wave polarization, and how this may be of importance for pulsars' physics [3] and light manipulation applications.

[1] Gueroult R., Rax J.-M., Zweben S. J. and Fisch, N. J., Plasma Phys. Control. Fusion, 2018, 60, 014018

[2] Rax J. M., Gueroult, R. and Fisch, N. J., Phys. Plasmas, 2017, 24, 032504

[3] Gueroult R., Shi Y., Rax J.-M. and Fisch N. J., Nat. Commun., 2019, 10, 3232



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Meeting ID: 977 3908 7835
Host: Cary Forest
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Physics ∩ ML Seminar
Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics
Time: 11:00 am
Place: Online Seminar: Please sign up for our mailing list at www.physicsmeetsml.org for zoom link
Speaker: Daniel Kunin and Hidenori Tanaka, Stanford University
Abstract: Understanding the dynamics of neural network parameters during training is one of the key challenges in building a theoretical foundation for deep learning. A central obstacle is that the motion of a network in high-dimensional parameter space undergoes discrete finite steps along complex stochastic gradients derived from real-world datasets. We circumvent this obstacle through a unifying theoretical framework based on intrinsic symmetries embedded in a network’s architecture that are present for any dataset. We show that any such symmetry imposes stringent geometric constraints on gradients and Hessians, leading to an associated conservation law in the continuous-time limit of stochastic gradient descent (SGD), akin to Noether’s theorem in physics. We further show that finite learning rates used in practice can actually break these symmetry induced conservation laws. We apply tools from finite difference methods to derive modified gradient flow, a differential equation that better approximates the numerical trajectory taken by SGD at finite learning rates. We combine modified gradient flow with our framework of symmetries to derive exact integral expressions for the dynamics of certain parameter combinations. We empirically validate our analytic expressions for learning dynamics on VGG-16 trained on Tiny ImageNet. Overall, by exploiting symmetry, our work demonstrates that we can analytically describe the learning dynamics of various parameter combinations at finite learning rates and batch sizes for state of the art architectures trained on any dataset.
Host: Gary Shiu
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Department Meeting
Time: 12:15 pm
Place: Virtual see "abstract" for connection info
Speaker: Sridhara Dasu, Department Chair
AIMEE N LEFKOW is inviting you to a scheduled Zoom meeting.

Topic: Department Meeting
Time: Jan 13, 2021 12:15 PM Central Time (US and Canada)
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Meeting ID: 924 9932 5588
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Host: Sridhara Dasu
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