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arXiv

Evidence-based prescriptive analytics, causal digital twin and a learning estimation algorithm

作     者:Madhavan, P.G. 

作者机构:Jin Innovation Seattle United States 

出 版 物:《arXiv》 (arXiv)

年 卷 期:2021年

核心收录:

主  题:Learning algorithms 

摘      要:Evidence-based Prescriptive Analytics (EbPA) is necessary to determine optimal operational set-points that will improve business productivity. EbPA results from what-if analysis and counterfactual experimentation on CAUSAL Digital Twins (CDTs) that quantify cause-effect relationships in the DYNAMICS of a system of connected assets. We describe the basics of Causality and Causal Graphs and develop a Learning Causal Digital Twin (LCDT) solution;our algorithm uses a simple recurrent neural network with some innovative modifications incorporating Causal Graph simulation. Since LCDT is a learning digital twin where parameters are learned online in real-time with minimal pre-configuration, the work of deploying digital twins will be significantly *** Codes 93-05 Copyright © 2021, The Authors. All rights reserved.

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