FEAT - Free energy Estimators with Adaptive Transport

José Miguel Hernández Lobato

  Universidad de Cambridge

Resumen/Abstract

We present Free energy Estimators with Adaptive Transport (FEAT), a novel framework for free energy estimation -- a critical challenge across scientific domains. FEAT leverages learned transports implemented via stochastic interpolants and provides consistent, minimum-variance estimators based on escorted Jarzynski equality and controlled Crooks theorem, alongside variational upper and lower bounds on free energy differences. Unifying equilibrium and non-equilibrium methods under a single theoretical framework, FEAT establishes a principled foundation for neural free energy calculations. Experimental validation on toy examples, molecular simulations, and quantum field theory demonstrates improvements over existing learning-based methods.

Profesor Proponente EPS: Daniel Hernández Lobato

Curriculum ponente

Jose Miguel Hernandez Lobato is a lecturer in Machine Learning at the Department of Engineering of the University of Cambridge. Before that, he was a postdoctoral fellow at the Harvard School of Applied Sciences and Engineering from Sept. 2014 to Sept. 2016. His research interests are in Bayesian optimization, scalable methods for approximate inference, and flexible probabilistic modeling of data. Jose Miguel's research is driven by machine learning applications to expensive optimal design problems in engineering. Before joining Harvard, Jose Miguel was a postdoctoral research associate at the Department of Engineering of the University of Cambridge where he worked in a collaboration project with the Indian multinational company Infosys Technologies. From December 2010 to May 2011, Jose Miguel was a teaching assistant at the Computer Science Department at Universidad Autónoma de Madrid (Spain), where he obtained his Ph.D. and M.Phil. in Computer Science in December 2010 and June 2007, respectively. Jose Miguel also received a B.Sc. in Computer Science from this institution in June 2004, with a special prize for the best academic record on graduation.