Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Simulação de Gêmeo Digital× | Simulação de Monte Carlo× | |
|---|---|---|
| Área≠ | Simulação | Tomada de decisão |
| Família≠ | Process / pipeline | MCDM |
| Ano de origem≠ | 2002 (concept); 2014 (white paper formalization) | 1949 |
| Autor original≠ | Michael Grieves (University of Michigan, 2002; white paper 2014) | Metropolis, N., Ulam, S. |
| Tipo≠ | Hybrid physics-based + machine-learning simulation | Robustness wrapper — Monte Carlo uncertainty propagation |
| Fonte seminal≠ | Grieves, M. (2014). Digital Twin: Manufacturing Excellence through Virtual Factory Replication. White Paper, University of Michigan. link ↗ | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Outros nomes≠ | Dijital İkiz Simülasyonu (Digital Twin), digital twin, digital shadow, cyber-physical twin | — |
| Relacionados≠ | 4 | 0 |
| Resumo≠ | 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. |
| ScholarGateConjunto de dados ↗ |
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