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.

Related papers

• Towards a Real-Time Simulation of Elastoplastic Deformation Using Multi-Task Neural Networks [0.0]
  This study introduces a surrogate modeling framework merging proper decomposition, long short-term memory networks, and multi-task learning, to accurately predict elastoplastic deformations in real-time. The framework achieves a mean absolute error below 0.40% across various state variables. In our use cases, a pre-trained multi-task model can effectively train additional variables with as few as 20 samples, demonstrating its deep understanding of complex scenarios.
  arXiv  Detail & Related papers  (2024-11-08T14:04:17Z)
• Investigating the Robustness of Counterfactual Learning to Rank Models: A Reproducibility Study [61.64685376882383]
  Counterfactual learning to rank (CLTR) has attracted extensive attention in the IR community for its ability to leverage massive logged user interaction data to train ranking models. This paper investigates the robustness of existing CLTR models in complex and diverse situations. We find that the DLA models and IPS-DCM show better robustness under various simulation settings than IPS-PBM and PRS with offline propensity estimation.
  arXiv  Detail & Related papers  (2024-04-04T10:54:38Z)
• HyperImpute: Generalized Iterative Imputation with Automatic Model Selection [77.86861638371926]
  We propose a generalized iterative imputation framework for adaptively and automatically configuring column-wise models. We provide a concrete implementation with out-of-the-box learners, simulators, and interfaces.
  arXiv  Detail & Related papers  (2022-06-15T19:10:35Z)
• Multi-fidelity Hierarchical Neural Processes [79.0284780825048]
  Multi-fidelity surrogate modeling reduces the computational cost by fusing different simulation outputs. We propose Multi-fidelity Hierarchical Neural Processes (MF-HNP), a unified neural latent variable model for multi-fidelity surrogate modeling. We evaluate MF-HNP on epidemiology and climate modeling tasks, achieving competitive performance in terms of accuracy and uncertainty estimation.
  arXiv  Detail & Related papers  (2022-06-10T04:54:13Z)
• Abstraction of Markov Population Dynamics via Generative Adversarial Nets [2.1485350418225244]
  A strategy to reduce computational load is to abstract the population model, replacing it with a simpler model, faster to simulate. Here we pursue this idea, building on previous works and constructing a generator capable of producing trajectories in continuous space and discrete time. This generator is learned automatically from simulations of the original model in a Generative Adversarial setting.
  arXiv  Detail & Related papers  (2021-06-24T12:57:49Z)
• Deep Bayesian Active Learning for Accelerating Stochastic Simulation [74.58219903138301]
  Interactive Neural Process (INP) is a deep active learning framework for simulations and with active learning approaches. For active learning, we propose a novel acquisition function, Latent Information Gain (LIG), calculated in the latent space of NP based models. The results demonstrate STNP outperforms the baselines in the learning setting and LIG achieves the state-of-the-art for active learning.
  arXiv  Detail & Related papers  (2021-06-05T01:31:51Z)
• Learning Accurate Business Process Simulation Models from Event Logs via Automated Process Discovery and Deep Learning [0.8164433158925593]
  Data-Driven Simulation (DDS) methods learn process simulation models from event logs. Deep Learning (DL) models are able to accurately capture such temporal dynamics. This paper presents a hybrid approach to learn process simulation models from event logs.
  arXiv  Detail & Related papers  (2021-03-22T15:34:57Z)
• Recurrent convolutional neural network for the surrogate modeling of subsurface flow simulation [0.0]
  We propose to combine SegNet with ConvLSTM layers for the surrogate modeling of numerical flow simulation. Results show that the proposed method improves the performance of SegNet based surrogate model remarkably when the output of the simulation is time series data.
  arXiv  Detail & Related papers  (2020-10-08T09:34:48Z)
• Goal-directed Generation of Discrete Structures with Conditional Generative Models [85.51463588099556]
  We introduce a novel approach to directly optimize a reinforcement learning objective, maximizing an expected reward. We test our methodology on two tasks: generating molecules with user-defined properties and identifying short python expressions which evaluate to a given target value.
  arXiv  Detail & Related papers  (2020-10-05T20:03:13Z)
• Real-Time Regression with Dividing Local Gaussian Processes [62.01822866877782]
  Local Gaussian processes are a novel, computationally efficient modeling approach based on Gaussian process regression. Due to an iterative, data-driven division of the input space, they achieve a sublinear computational complexity in the total number of training points in practice. A numerical evaluation on real-world data sets shows their advantages over other state-of-the-art methods in terms of accuracy as well as prediction and update speed.
  arXiv  Detail & Related papers  (2020-06-16T18:43:31Z)
• Using Machine Learning to Emulate Agent-Based Simulations [0.0]
  We evaluate the performance of multiple machine-learning methods as statistical emulators for use in the analysis of agent-based models (ABMs) We propose that agent-based modelling would benefit from using machine-learning methods for emulation, as this can facilitate more robust sensitivity analyses for the models.
  arXiv  Detail & Related papers  (2020-05-05T11:48:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.