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Found 5 papers, 0 papers with code
qPOTS: Efficient batch multiobjective Bayesian optimization via Pareto optimal Thompson sampling
Classical evolutionary approaches for multiobjective optimization are quite effective but incur a lot of queries to the objectives; this can be prohibitive when objectives are expensive oracles.
Data-Driven Wind Turbine Wake Modeling via Probabilistic Machine Learning
Physics-based models that capture the wake flow-field with high-fidelity are computationally very expensive to perform layout optimization of wind farms, and, thus, data-driven reduced order models can represent an efficient alternative for simulating wind farms.
Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization
Furthermore, we show that multiple optimization problems can be solved with the same machine learning model with high accuracy, to amortize the offline costs associated with constructing our models.
Recursive Two-Step Lookahead Expected Payoff for Time-Dependent Bayesian Optimization
We propose a novel Bayesian method to solve the maximization of a time-dependent expensive-to-evaluate oracle.
Aerodynamic Data Fusion Towards the Digital Twin Paradigm
For example, two sources of pressure fields about an aircraft are fused based on measured forces and moments from a wind-tunnel experiment.
