Machine Learning and the Physical Sciences

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Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. Here I will show how we can use physics inspired deep learning algorithms like Boltzmann machines to study study problems in quantum many body physics, quantum computing, and chemical and material physics.

The lecture will be given by Morten Hjorth-Jensen, Michigan State University and UiO.

Morten is a theoretical physicist with a strong interest in computational physics and many-body theory in general, and the nuclear many-body problem and nuclear structure problems in particular. This means that he study various methods for solving either Schrödinger's equation or Dirac's equation for many interacting particles, spanning from algorithmic aspects to the mathematical properties of such methods. The latter also leads to a strong interest in computational physics as well as computational aspects of quantum mechanical methods. he share his time equally between Michigan State University in the US (January-June) and the University of Oslo, Norway (July-December).