Analyzing stochastic computer models: A review with opportunities
Analyzing Stochastic Computer Models: A Review with Opportunities Page 1 Statistical
Science 2022, Vol. 37, No. 1, 64–89 https://doi.org/10.1214/21-STS822 © Institute of …
Science 2022, Vol. 37, No. 1, 64–89 https://doi.org/10.1214/21-STS822 © Institute of …
Practical Bayesian optimization for model fitting with Bayesian adaptive direct search
Computational models in fields such as computational neuroscience are often evaluated via
stochastic simulation or numerical approximation. Fitting these models implies a difficult …
stochastic simulation or numerical approximation. Fitting these models implies a difficult …
Comparison of kriging-based algorithms for simulation optimization with heterogeneous noise
In this article we investigate the unconstrained optimization (minimization) of the
performance of a system that is modeled through a discrete-event simulation. In recent …
performance of a system that is modeled through a discrete-event simulation. In recent …
An efficient machine learning approach to establish structure-property linkages
Full-field simulations with synthetic microstructure offer unique opportunities in predicting
and understanding the linkage between microstructural variables and properties of a …
and understanding the linkage between microstructural variables and properties of a …
Revisiting Bayesian optimization in the light of the COCO benchmark
It is commonly believed that Bayesian optimization (BO) algorithms are highly efficient for
optimizing numerically costly functions. However, BO is not often compared to widely …
optimizing numerically costly functions. However, BO is not often compared to widely …
TREGO: a trust-region framework for efficient global optimization
Efficient global optimization (EGO) is the canonical form of Bayesian optimization that has
been successfully applied to solve global optimization of expensive-to-evaluate black-box …
been successfully applied to solve global optimization of expensive-to-evaluate black-box …
Bayesian calibration of force fields for molecular simulations
Molecular simulations are one of the most prominent discovery tools in science and
engineering, widely adopted in applications ranging from drug discovery to materials …
engineering, widely adopted in applications ranging from drug discovery to materials …
Bayesian approach in predicting mechanical properties of materials: Application to dual phase steels
An essential task in materials science and engineering is in quantifying the linkages
between physical variables of a material to its properties. These linkages are both complex …
between physical variables of a material to its properties. These linkages are both complex …
Influence of the variation of geometrical and topological traits on light interception efficiency of apple trees: sensitivity analysis and metamodelling for ideotype …
Background and Aims The impact of a fruit tree's architecture on its performance is still under
debate, especially with regard to the definition of varietal ideotypes and the selection of …
debate, especially with regard to the definition of varietal ideotypes and the selection of …
Multi-fidelity modeling of probabilistic aerodynamic databases for use in aerospace engineering
J Mukhopadhaya, BT Whitehead… - International Journal …, 2020 - dl.begellhouse.com
Explicit quantification of uncertainty in engineering simulations is being increasingly used to
inform robust and reliable design practices. In the aerospace industry, computationally …
inform robust and reliable design practices. In the aerospace industry, computationally …