Salīdzināt metodes

	Digitālā dvīņa simulācija	Valsts telpas modelis (Kalmana filtrs)
Nozare	Simulācija	Ekonometrija
Saime	Process / pipeline	Regression model
Izcelsmes gads	2002 (concept); 2014 (white paper formalization)	1990
Autors	Michael Grieves (University of Michigan, 2002; white paper 2014)	Harvey; Durbin & Koopman (state space treatment); Kalman filter
Tips	Hybrid physics-based + machine-learning simulation	State space time series model
Pirmavots	Grieves, M. (2014). Digital Twin: Manufacturing Excellence through Virtual Factory Replication. White Paper, University of Michigan.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
Citi nosaukumi	Dijital İkiz Simülasyonu (Digital Twin), digital twin, digital shadow, cyber-physical twin	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
Saistītās	4	4
Kopsavilkums	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.	A state space model is a general time series framework that describes a series through unobserved (latent) state variables linked by a measurement equation and a transition equation, with the states estimated in real time by the Kalman filter. Developed in the state space tradition of Harvey (1990) and Durbin & Koopman (2012), it nests ARIMA and exponential smoothing as special cases.