4 papers
Trajectory-Oriented Optimization Via Adaptive Thompson Sampling And Grid Refinement: A Tutorial With The ADAPTIVE\_TS Package
David O'Gara, Arindam Fadikar, Mickaël Binois +2
Stochastic simulators are increasingly used to expand the frontier of scientific knowledge and inform decision-making across real-world contexts. Simulator calibration, a process b…
Staying on Track: Efficient Trajectory Discovery with Adaptive Batch Sampling
Arindam Fadikar, Abby Stevens, Mickael Binois +3
Bayesian optimization (BO) is a powerful framework for estimating parameters of expensive simulation models, particularly in settings where the likelihood is intractable and evalua…
Scaled Block Vecchia Approximation for High-Dimensional Gaussian Process Emulation on GPUs
Qilong Pan, Sameh Abdulah, Mustafa Abduljabbar +8
Emulating computationally intensive scientific simulations is crucial for enabling uncertainty quantification, optimization, and informed decision-making at scale. Gaussian Process…
Gearing Gaussian process modeling and sequential design towards stochastic simulators
Mickael Binois, Arindam Fadikar, Abby Stevens
This chapter presents specific aspects of Gaussian process modeling in the presence of complex noise. Starting from the standard homoscedastic model, various generalizations from t…