Using Machine Learning to Emulate Agent-Based Simulations

• URL: http://arxiv.org/abs/2005.02077v2
• Date: Sat, 24 Jul 2021 16:04:13 GMT
• Title: Using Machine Learning to Emulate Agent-Based Simulations
• Authors: Claudio Angione, Eric Silverman, Elisabeth Yaneske
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this proof-of-concept work, we evaluate the performance of multiple machine-learning methods as statistical emulators for use in the analysis of agent-based models (ABMs). Analysing ABM outputs can be challenging, as the relationships between input parameters can be non-linear or even chaotic even in relatively simple models, and each model run can require significant CPU time. Statistical emulation, in which a statistical model of the ABM is constructed to facilitate detailed model analyses, has been proposed as an alternative to computationally costly Monte Carlo methods. Here we compare multiple machine-learning methods for ABM emulation in order to determine the approaches best suited to emulating the complex behaviour of ABMs. Our results suggest that, in most scenarios, artificial neural networks (ANNs) and gradient-boosted trees outperform Gaussian process emulators, currently the most commonly used method for the emulation of complex computational models. ANNs produced the most accurate model replications in scenarios with high numbers of model runs, although training times were longer than the other methods. 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 while also reducing CPU time consumption when calibrating and analysing the simulation.

Related papers

• Embed and Emulate: Contrastive representations for simulation-based inference [11.543221890134399]
  This paper introduces Embed and Emulate (E&E), a new simulation-based inference ( SBI) method based on contrastive learning. E&E learns a low-dimensional latent embedding of the data and a corresponding fast emulator in the latent space. We demonstrate superior performance over existing methods in a realistic, non-identifiable parameter estimation task.
  arXiv  Detail & Related papers  (2024-09-27T02:37:01Z)
• Diffusion posterior sampling for simulation-based inference in tall data settings [53.17563688225137]
  Simulation-based inference ( SBI) is capable of approximating the posterior distribution that relates input parameters to a given observation. In this work, we consider a tall data extension in which multiple observations are available to better infer the parameters of the model. We compare our method to recently proposed competing approaches on various numerical experiments and demonstrate its superiority in terms of numerical stability and computational cost.
  arXiv  Detail & Related papers  (2024-04-11T09:23:36Z)
• A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics [73.35846234413611]
  In drug discovery, molecular dynamics (MD) simulation provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites. We propose NeuralMD, the first machine learning (ML) surrogate that can facilitate numerical MD and provide accurate simulations in protein-ligand binding dynamics. We demonstrate the efficiency and effectiveness of NeuralMD, achieving over 1K$times$ speedup compared to standard numerical MD simulations.
  arXiv  Detail & Related papers  (2024-01-26T09:35:17Z)
• Online Variational Sequential Monte Carlo [49.97673761305336]
  We build upon the variational sequential Monte Carlo (VSMC) method, which provides computationally efficient and accurate model parameter estimation and Bayesian latent-state inference. Online VSMC is capable of performing efficiently, entirely on-the-fly, both parameter estimation and particle proposal adaptation.
  arXiv  Detail & Related papers  (2023-12-19T21:45:38Z)
• Learning to Simulate: Generative Metamodeling via Quantile Regression [2.2518304637809714]
  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.
  arXiv  Detail & Related papers  (2023-11-29T16:46:24Z)
• Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research [76.93956925360638]
  Waymax is a new data-driven simulator for autonomous driving in multi-agent scenes. It runs entirely on hardware accelerators such as TPUs/GPUs and supports in-graph simulation for training. We benchmark a suite of popular imitation and reinforcement learning algorithms with ablation studies on different design decisions.
  arXiv  Detail & Related papers  (2023-10-12T20:49:15Z)
• Finding the Perfect Fit: Applying Regression Models to ClimateBench v1.0 [0.0]
  ClimateBench is a benchmarking dataset for evaluating the performance of machine learning emulators designed for climate data. This study focuses on evaluating non-linear regression models using the aforementioned dataset.
  arXiv  Detail & Related papers  (2023-08-23T01:08:01Z)
• Real-to-Sim: Predicting Residual Errors of Robotic Systems with Sparse Data using a Learning-based Unscented Kalman Filter [65.93205328894608]
  We learn the residual errors between a dynamic and/or simulator model and the real robot. We show that with the learned residual errors, we can further close the reality gap between dynamic models, simulations, and actual hardware.
  arXiv  Detail & Related papers  (2022-09-07T15:15:12Z)
• 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)
• Model-based Policy Optimization with Unsupervised Model Adaptation [37.09948645461043]
  We investigate how to bridge the gap between real and simulated data due to inaccurate model estimation for better policy optimization. We propose a novel model-based reinforcement learning framework AMPO, which introduces unsupervised model adaptation. Our approach achieves state-of-the-art performance in terms of sample efficiency on a range of continuous control benchmark tasks.
  arXiv  Detail & Related papers  (2020-10-19T14:19:42Z)
• Hybrid modeling: Applications in real-time diagnosis [64.5040763067757]
  We outline a novel hybrid modeling approach that combines machine learning inspired models and physics-based models. We are using such models for real-time diagnosis applications.
  arXiv  Detail & Related papers  (2020-03-04T00:44:57Z)

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.