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arXiv · 1910.13496

Asymptotically unbiased estimation of physical observables with neural samplers

Kim A. Nicoli·Shinichi Nakajima·Nils Strodthoff·Wojciech Samek·Klaus-Robert Müller·Pan Kessel
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Abstract

We propose a general framework for the estimation of observables with generative neural samplers focusing on modern deep generative neural networks that provide an exact sampling probability. In this framework, we present asymptotically unbiased estimators for generic observables, including those that explicitly depend on the partition function such as free energy or entropy, and derive corresponding variance estimators. We demonstrate their practical applicability by numerical experiments for the 2d Ising model which highlight the superiority over existing methods. Our approach greatly enhances the applicability of generative neural samplers to real-world physical systems.

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BibTeXRIS

Kim A. Nicoli, Shinichi Nakajima, Nils Strodthoff, Wojciech Samek, Klaus-Robert Müller, Pan Kessel. 2019-10-29. Asymptotically unbiased estimation of physical observables with neural samplers. https://doi.org/10.1103/physreve.101.023304

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