Toward the Thermodynamic Limit: Neural Operators for Non-equilibrium Dynamics of Mott Insulators
Miles Waugh, Chuwei Wang, Radu Andrei, Nusair Islam, Taylor Lee Patti, Eugene Demler, Anima Anandkumar

TL;DR
This paper demonstrates that Fourier Neural Operators can learn to predict the non-equilibrium dynamics of Mott insulators in the thermodynamic limit, enabling large-scale simulations beyond traditional computational capabilities.
Contribution
The study introduces the use of Fourier Neural Operators to accurately predict large-scale non-equilibrium dynamics of Mott insulators from small system training data.
Findings
Model predicts 1024x1024 lattice behavior in seconds
Neural operators generalize zero-shot to larger systems
Predictions match theoretical expectations for key observables
Abstract
Mott insulators exhibit complex photoexcitation dynamics under intense optical driving, with potential implications for carrier multiplication beyond the Shockley-Queisser limit. Probing these nonequilibrium processes requires access to the thermodynamic limit, where the number of lattice sites becomes arbitrarily large, but conventional solvers are constrained to small systems due to the exponential growth of the Hilbert space. Fourier Neural Operators (FNOs), originally developed for solving partial differential equations, naturally accommodate inputs of varying resolution and are capable of capturing nonlocal effects. Here, we employ FNOs to learn the mapping from noise-perturbed ground-state momentum distributions to their post-pulse counterparts across a range of interaction strengths and driving parameters. Trained only on small lattices, the model generalizes zero-shot to much…
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Taxonomy
TopicsQuantum many-body systems · Neural Networks and Reservoir Computing · Machine Learning in Materials Science
