AI for physics & physics for AI

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• Prof. Max Tegmark (MIT)
• Tuesday 26 January 2021, 16:00-17:00
• https://cern.zoom.us/j/61144924828?pwd=WDB6QmFCMEZiSEN5Q0k2aklWSjk5Zz09.

If you have a question about this talk, please contact William Fawcett.

A central goal of physics is to discover mathematical patterns in data. For example, after four years of analyzing data tables on planetary orbits, Johannes Kepler started a scientific revolution in 1605 by discovering that Mars’ orbit was an ellipse. I describe how we can automate such tasks with machine learning and not only discover symbolic formulas accurately matching datasets (so-called symbolic regression), equations of motion and conserved quantities, but also auto-discover which degrees of freedom are most useful for predicting time evolution (for example, optimal generalized coordinates extracted from video data). The methods I present exploit numerous ideas from physics to recursively simplify neural networks, ranging from symmetries to differentiable manifolds, curvature and topological defects, and also take advantage of mathematical insights from knot theory and graph modularity.

This talk is part of the Cavendish HEP Seminars series.

This talk is included in these lists:

• All Cavendish Laboratory Seminars
• All Talks (aka the CURE list)
• Cavendish HEP Seminars
• Centre for Health Leadership and Enterprise
• Featured lists
• ME Seminar
• Neurons, Fake News, DNA and your iPhone: The Mathematics of Information
• School of Physical Sciences
• Thin Film Magnetic Talks
• Zoom
• few29
• https://cern.zoom.us/j/61144924828?pwd=WDB6QmFCMEZiSEN5Q0k2aklWSjk5Zz09

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