Introduction
Machine learning is no longer restricted to data analysis, currently being used in theory, experiment and simulation - a sign that AI is becoming pervasive in all traditional aspects of research. But are theorists, experimentalists and computational scientists aware of each other’s problems, and the solutions developed to tackle them? Are researchers working in different areas of physics aware of developments in other areas?
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Chair - Benjamin Joachimi
About the event
With the bigger and better observatories and state-of-the-art large-scale simulations, researchers in astrophysics and cosmology need to handle and analyse ever-growing volumes of data. These communities have been pioneering new data analysis methods, planning infrastructure to support the huge data-pipelines and building a community approach to tackling their problems. What can the wider physics and AI communities learn from their experience and how can they contribute to the ongoing efforts?
