Learning Physics with Deep Neural Networks

  • Speaker
  • Stéphane Mallat, Ph.D.École Normale Supérieure de Paris (ENS)
Date & Time


Gerald D. Fischbach Auditorium
160 5th Ave
New York, NY 10010 United States

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TEA: 4:15-5:00pm LECTURE: 5:00-6:15pm

About Mathematics and Physical Sciences

Mathematics and Physical Sciences lectures are open to the public and are held at the Gerald D. Fischbach Auditorium at the Simons Foundation headquarters in New York City. Tea is served prior to each lecture.

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Can we learn physical properties from data? Machine learning offers a solution. It has many similarities with physics, requiring the approximation of functionals which depend on large numbers of variables, such as millions of pixels in images, letters in text, or particles in a physical system. Machine learning algorithms have considerably improved in the last 10 years through the processing of massive amounts of data. In particular, deep neural networks have spectacular applications, such as image classification and medical, industrial and physical data analysis.

In this lecture, Stéphane Mallat will show how machine learning can be applied to statistical physics, turbulent fluids and quantum chemistry. Beyond applications, he will highlight common mathematical approaches in physics and machine learning to overcome the issue of dimensionality. Two central pillars of such approaches are finding symmetries and separating phenomena at different scales. He will show that these pillars also govern the architecture and properties of deep convolutional neural networks.

About the Speaker

Mallat received his Ph.D. in electrical engineering from the University of Pennsylvania in 1988. He was then a professor at the Courant Institute of Mathematical Sciences until 1994. In 1995, he became a professor in applied mathematics at Ecole Polytechnique, Paris, and department chair in 2001. From 2001 to 2007 he was co-founder and CEO of a semiconductor start-up company. From 2012 to 2017 he was professor in the computer science department of Ecole Normale Supérieure in Paris. He has held the data sciences chair at the Collège de France since 2017.
Mallat’s research interests include machine learning, signal processing and harmonic analysis. He is a member of the French Academy of Sciences, a foreign member of the US National Academy of Engineering, an IEEE Fellow and an EUSIPCO Fellow. In 1997, he received the Outstanding Achievement Award from the SPIE Society. He was a plenary lecturer at the International Congress of Mathematicians in 1998. He also received the 2004 European IST Grand prize, the 2004 INIST-CNRS prize for the most-cited French researcher in engineering and computer science, the 2007 EADS grand prize of the French Academy of Sciences, the 2013 Medal of Innovation of the CNRS, and the 2014 IEEE Signal Sustained Impact Paper Award.
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