[PDF][PDF] Digital Twin: What It Is, Why Do It, and Research Opportunities for Operations Research
The concept of a Digital Twin (DT) has stood out among the emerging digitization
technologies and been embraced by US and EU governments and companies. Practitioners …
technologies and been embraced by US and EU governments and companies. Practitioners …
Blackbox Simulation Optimization
Simulation optimization is a widely used tool in the analysis and optimization of complex
stochastic systems. The majority of the previous works on simulation optimization rely …
stochastic systems. The majority of the previous works on simulation optimization rely …
Framing climate uncertainty: socio-economic and climate scenarios in vulnerability and adaptation assessments
F Berkhout, B van den Hurk, J Bessembinder… - Regional environmental …, 2014 - Springer
Scenarios have become a powerful tool in integrated assessment and policy analysis for
climate change. Socio-economic and climate scenarios are often combined to assess …
climate change. Socio-economic and climate scenarios are often combined to assess …
Simulation optimization in the new era of AI
We review simulation optimization methods and discuss how these methods underpin
modern artificial intelligence (AI) techniques. In particular, we focus on three areas …
modern artificial intelligence (AI) techniques. In particular, we focus on three areas …
Adaptive importance sampling for efficient stochastic root finding and quantile estimation
In solving simulation-based stochastic root-finding or optimization problems that involve rare
events, such as in extreme quantile estimation, running crude Monte Carlo can be …
events, such as in extreme quantile estimation, running crude Monte Carlo can be …
Noise optimization in artificial neural networks
Artificial neural network (ANN) has been widely used in automation. However, the
vulnerability of ANN under certain attacks poses a security threat to critical automation …
vulnerability of ANN under certain attacks poses a security threat to critical automation …
Review of Large-Scale Simulation Optimization
Large-scale simulation optimization (SO) problems encompass both large-scale ranking-
and-selection problems and high-dimensional discrete or continuous SO problems …
and-selection problems and high-dimensional discrete or continuous SO problems …
Monte Carlo and quasi–Monte Carlo density estimation via conditioning
P L'Ecuyer, F Puchhammer… - INFORMS Journal on …, 2022 - pubsonline.informs.org
Estimating the unknown density from which a given independent sample originates is more
difficult than estimating the mean in the sense that, for the best popular nonparametric …
difficult than estimating the mean in the sense that, for the best popular nonparametric …
History of seeking better solutions, AKA simulation optimization
MC Fu, SG Henderson - 2017 Winter Simulation Conference …, 2017 - ieeexplore.ieee.org
Simulation optimization-arguably the ultimate aim of most simulation users-has had a long
and illustrious history closely tied with the 50 years of the Winter Simulation Conference …
and illustrious history closely tied with the 50 years of the Winter Simulation Conference …
Maximum likelihood estimation by Monte Carlo simulation: Toward data-driven stochastic modeling
We propose a gradient-based simulated maximum likelihood estimation to estimate
unknown parameters in a stochastic model without assuming that the likelihood function of …
unknown parameters in a stochastic model without assuming that the likelihood function of …