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| Digital-Twin-Simulation× | System Dynamics× | |
|---|---|---|
| Fachgebiet | Simulation | Simulation |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 2002 (concept); 2014 (white paper formalization) | 1961 |
| Urheber≠ | Michael Grieves (University of Michigan, 2002; white paper 2014) | Jay W. Forrester |
| Typ≠ | Hybrid physics-based + machine-learning simulation | Continuous simulation / feedback modelling |
| Wegweisende Quelle≠ | Grieves, M. (2014). Digital Twin: Manufacturing Excellence through Virtual Factory Replication. White Paper, University of Michigan. link ↗ | Sterman, J.D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin McGraw-Hill. ISBN: 978-0072389159 |
| Aliasnamen | Dijital İkiz Simülasyonu (Digital Twin), digital twin, digital shadow, cyber-physical twin | stock-flow modelling, Sistem Dinamiği (Stock-Flow Modelleme), SD modelling, feedback simulation |
| Verwandt≠ | 4 | 3 |
| Zusammenfassung≠ | Digital Twin Simulation, first conceptualised by Michael Grieves at the University of Michigan around 2002 and formally described in his 2014 white paper, creates a continuously updated virtual copy of a physical system by fusing real-time sensor data with a mechanistic (physics-based) model and machine-learning components. The twin mirrors the physical asset's current state and projects its future behaviour, enabling fault detection, predictive maintenance, and operational optimisation without disrupting the real system. | System dynamics is a continuous simulation method, developed by Jay W. Forrester at MIT in 1961, that represents a complex system through stocks (accumulations), flows (rates of change), and feedback loops. By expressing these relationships as coupled ordinary differential equations, it reproduces how policies, delays, and nonlinear feedbacks drive system behaviour over time — making it a cornerstone tool in policy analysis, organisational modelling, and sustainability research. |
| ScholarGateDatensatz ↗ |
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