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Challenging problems in particle and nuclear physics, astronomy, condense matter and other branches of physics are tackled with AI.

Speaker:Dr Liliana Teodorescu, 成人直播app

Applying AI to physics problems is a common approach by now. Challenging problems in particle and nuclear physics, astronomy, condense matter and other branches of physics are tackled with AI. Physics-informed machine learning which incorporates physics knowledge and constraints into the design of the machine learning algorithms brings further benefits in solving physics problems. The benefits of the relationship between AI and Physics are bidirectional, though. Physics concepts and phenomena are used as inspiration for novel AI algorithms which aim to harvest the predictive power of the physics laws. Physics-inspired machine learning is an exciting and fast developing subfield. This interplay between the two fields is explored in this talk. Examples of applications of AI to physics problems as well as Physics-inspired AI algorithms will be discussed probing the mutual benefits for the two fields.

Dr. Liliana Teodorescu is a physicist with diverse research experience acquired participating in large-scale international particle and nuclear physics projects in Europe and United States. She combines physics with computer science and engineering both in her research and teaching. She has a particular interest in the development of machine learning algorithms for particle and nuclear physics experiments, and their extensions to solving real-world problems. She has started in this field well before the current machine learning revolution, when she has pioneered the application of Gene Expression Programming (a variant of Evolutionary Computation) as a machine learning algorithm to particle physics. She is also interested in combining physics with computer science in other innovative ways, investigating physics-inspired computer algorithms.

Moderator: Dr Nadine Aburumman (Lecturer in Computer Science, 成人直播app)