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Abstract

This thesis explores advanced generative modeling techniques with a focus on scientific applications. It addresses two main areas: free-form flows and machine learning applications in particle physics.

Free-form flows are a novel family of generative models that combine the benefits of normalizing flows—exact maximum likelihood training and fast generation—with unrestricted neural network architectures. This approach overcomes the limitations of traditional normalizing flows, allowing for more flexible and domain-specific models.

The thesis also presents a collection of machine learning applications in particle physics, leveraging models with rich representational spaces. These techniques aim to accelerate the processing of vast data streams from the Large Hadron Collider, potentially uncovering new physics beyond the Standard Model.

By advancing both the theoretical foundations and practical implementations of generative models, this work contributes to their increased adoption and impact across diverse scientific disciplines. Applications range from chemistry to particle physics, demonstrating the broad potential of these methods in modeling complex data distributions.