Methods for enabling real-time analysis in digital twins: A literature review

2024/04/04 by Mohammad Sadegh Es-haghi, Mohammad Sadegh Eshaghi, Cosmin Anitescu +1 · 4 citations
Engineering · #Digital Transformation in Industry #Advancements in Semiconductor Devices and Circuit Design #Electrostatic Discharge in Electronics

paper · doi:10.1016/j.compstruc.2024.107342

Abstract

This paper presents a literature review on methods for enabling real-time analysis in digital twins, which are virtual models of physical systems. The advantages of digital twins are numerous, including cost reduction, risk mitigation, efficiency enhancement, and decision-making support. However, their implementation faces challenges such as the need for real-time data analysis, resource limitations, and data uncertainty. The paper focuses on methods for reducing computational demands, which have not been systematically discussed in the literature. The paper reviews and categorizes methods and tools for accelerating the modeling of physical phenomena and reducing the computational needs of digital twins.

Citations

• Galerkin Proper Orthogonal Decomposition Methods for a General Equation in Fluid Dynamics
• Automatic choice of global shape functions in structural analysis
• Balanced Model Reduction via the Proper Orthogonal Decomposition
• Reduced Basis Technique for Nonlinear Analysis of Structures
• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• DAS-PINNs: A deep adaptive sampling method for solving high-dimensional partial differential equations

Cited by

• A Continual Validation, Updating, and Decision-Making Framework for Self-Adaptive Digital Twins via Robust Model Predictive Control: A Case Study in Additive Manufacturing
• NOWS: Neural Operator Warm Starts for Accelerating Iterative Solvers
• Improving performance estimation of a PCM-integrated solar chimney through reduced-order based data assimilation
• Physics‐Informed Digital Twin for Predicting Permafrost Thermodynamic Characteristics Under an Embankment Road in Utqiaġvik, Alaska

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