Learning to Simulate: Generative Metamodeling via Quantile Regression

• URL: http://arxiv.org/abs/2311.17797v1
• Date: Wed, 29 Nov 2023 16:46:24 GMT
• Title: Learning to Simulate: Generative Metamodeling via Quantile Regression
• Authors: L. Jeff Hong and Yanxi Hou and Qingkai Zhang and Xiaowei Zhang
• Abstract summary: We propose a new metamodeling concept, called generative metamodeling, which aims to construct a "fast simulator of the simulator" Once constructed, a generative metamodel can generate a large amount of random outputs as soon as the inputs are specified. We propose a new algorithm -- quantile-regression-based generative metamodeling (QRGMM) -- and study its convergence and rate of convergence.
• Score: 2.2518304637809714
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Stochastic simulation models, while effective in capturing the dynamics of complex systems, are often too slow to run for real-time decision-making. Metamodeling techniques are widely used to learn the relationship between a summary statistic of the outputs (e.g., the mean or quantile) and the inputs of the simulator, so that it can be used in real time. However, this methodology requires the knowledge of an appropriate summary statistic in advance, making it inflexible for many practical situations. In this paper, we propose a new metamodeling concept, called generative metamodeling, which aims to construct a "fast simulator of the simulator". This technique can generate random outputs substantially faster than the original simulation model, while retaining an approximately equal conditional distribution given the same inputs. Once constructed, a generative metamodel can instantaneously generate a large amount of random outputs as soon as the inputs are specified, thereby facilitating the immediate computation of any summary statistic for real-time decision-making. Furthermore, we propose a new algorithm -- quantile-regression-based generative metamodeling (QRGMM) -- and study its convergence and rate of convergence. Extensive numerical experiments are conducted to investigate the empirical performance of QRGMM, compare it with other state-of-the-art generative algorithms, and demonstrate its usefulness in practical real-time decision-making.

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