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page	ML in the Sciences	1	General information about the seminar

Machine Learning in the Sciences

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Introduction

Deep Learning has been successfully applied in a wide range of use cases and specifically in applications involving visual and textual data. Modern machine translation systems and search engines, for example, are using language models trained on large text corpora.

Increasingly, deep learning is also applied to problems arising in the sciences and engineering. For instance, deep learning for graphs is used to learn simulators from data. The figure on the right shows a particle simulation obtained from a graph neural network trained on simulation traces from a numerical solver. The advantage of using deep learning in this context is its ability to integrate the simulation into a larger neural network with a corresponding loss function and train the resulting model end-to-end. Other applications of deep learning can be found in chemistry, the biomedical sciences, drug discovery, and engineering disciplines, where use cases range from drug-protein interaction prediction to modeling fluid dynamics. Since machine learning and specifically deep learning will be increasingly used in disciplines of science and engineering, this seminar’s goal is to provide an overview of applications, to give students a deeper understanding of recent work, and to have an opportunity to learn how to read, analyze, and engage with scientific papers.

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