[21] Michela Paganini - Machine Learning Solutions for High Energy Physics
The Thesis Review
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Innovating Particle Physics with GANs
This chapter explores the incorporation of Generative Adversarial Networks (GANs) into simulations for particle collisions, highlighting the significant improvements in computational efficiency. The speaker discusses the challenges and successes of applying GANs to enhance simulation models, as well as the need for validation in high-energy physics. It also reflects on the cultural dynamics within the field and the evolution of machine learning methods in relation to traditional practices.
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