Bandingkan metode

	Simulasi Digital Twin	Simulasi Monte Carlo
Bidang	Simulasi	Pengambilan Keputusan
Keluarga	Process / pipeline	MCDM
Tahun asal	2002 (concept); 2014 (white paper formalization)	1949
Pencetus	Michael Grieves (University of Michigan, 2002; white paper 2014)	Metropolis, N., Ulam, S.
Tipe	Hybrid physics-based + machine-learning simulation	Robustness wrapper — Monte Carlo uncertainty propagation
Sumber perintis	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
Alias	Dijital İkiz Simülasyonu (Digital Twin), digital twin, digital shadow, cyber-physical twin	—
Terkait	4	0
Ringkasan	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.