6 papers
Sampling from Gaussian Processes: A Tutorial and Applications in Global Sensitivity Analysis and Optimization
Bach Do, Nafeezat A. Ajenifuja, Taiwo A. Adebiyi +1
High-fidelity simulations and physical experiments are essential for engineering analysis and design, yet their high cost often makes two critical tasks--global sensitivity analysi…
Multi-fidelity Bayesian Optimization: A Review
Bach Do, Ruda Zhang
Resided at the intersection of multi-fidelity optimization (MFO) and Bayesian optimization (BO), MF BO has found a niche in solving expensive engineering design optimization proble…
Achieving high-performance polarization-independent nonreciprocal thermal radiation with pattern-free heterostructures
Bach Do, Bardia Nabavi, Sina Jafari Ghalekohneh +3
Many advanced energy harvesting technologies rely on advanced control of thermal emission. Recently, it has been shown that the emissivity and absorptivity of thermal emitters can…
Optimizing Posterior Samples for Bayesian Optimization via Rootfinding
Taiwo A. Adebiyi, Bach Do, Ruda Zhang
Bayesian optimization devolves the global optimization of a costly objective function to the global optimization of a sequence of acquisition functions. This inner-loop optimizatio…
Epsilon-Greedy Thompson Sampling to Bayesian Optimization
Bach Do, Taiwo Adebiyi, Ruda Zhang
Bayesian optimization (BO) has become a powerful tool for solving simulation-based engineering optimization problems thanks to its ability to integrate physical and mathematical un…
Gaussian Process Thompson Sampling via Rootfinding
Taiwo A. Adebiyi, Bach Do, Ruda Zhang
Thompson sampling (TS) is a simple, effective stochastic policy in Bayesian decision making. It samples the posterior belief about the reward profile and optimizes the sample to ob…