Non-reversible lifts of reversible diffusion processes and relaxation times

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• Andreas Eberle (University of Bonn)
• Monday 15 July 2024, 14:00-15:00
• External.

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DMLW01 - International workshop on diffusions in machine learning: foundations, generative models, and optimisation

We propose a new concept of lifts of reversible diffusion processes and show that various well-known non-reversible Markov processes arising in applications are lifts in this sense of simple reversible diffusions. Furthermore, we introduce a concept of non-asymptotic relaxation times and show that these can at most be reduced by a square root through lifting, generalising a related result in discrete time. Finally, we demonstrate how the recently developed approach to quantitative hypocoercivity based on space-time Poincaré inequalities can be rephrased and simplified in the language of lifts and how it can be applied to find optimal lifts.

This talk is part of the Isaac Newton Institute Seminar Series series.

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