We develop a framework for automated optimization of stochastic simulation models using Response Surface Methodology. The framework is especially intended for simulation models wh...
H. Gonda Neddermeijer, Gerrit J. van Oortmarssen, ...
We present a general boosting method extending functional gradient boosting to optimize complex loss functions that are encountered in many machine learning problems. Our approach...
Wildfire propagation is a complex process influenced by many factors. Simulation models of wildfire spread, such as DEVS-FIRE, are important tools for studying fire behavior. This...
The Data Driven Design Optimization Methodology (DDDOM) is a Dynamic Data Driven Application System (DDDAS) developed for engineering design optimization. The DDDOM synergizes expe...
, Yunde Jia Model structure selection is currently an open problem in modeling data via Gaussian Mixture Models (GMM). This paper proposes a discriminative method to select GMM st...