Linganisha mbinu

	Mahitaji ya Digital Twin	Uiguzi wa Monte Carlo
Nyanja	Uigaji	Ufanyaji Maamuzi
Familia	Process / pipeline	MCDM
Mwaka wa asili	2002 (concept); 2014 (white paper formalization)	1949
Mwanzilishi	Michael Grieves (University of Michigan, 2002; white paper 2014)	Metropolis, N., Ulam, S.
Aina	Hybrid physics-based + machine-learning simulation	Robustness wrapper — Monte Carlo uncertainty propagation
Chanzo asilia	Grieves, M. (2014). Digital Twin: Manufacturing Excellence through Virtual Factory Replication. White Paper, University of Michigan.	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Majina mbadala	Dijital İkiz Simülasyonu (Digital Twin), digital twin, digital shadow, cyber-physical twin	—
Zinazohusiana	4	0
Muhtasari	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.	MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.