132 citations · 317 across the 27 of their papers we have counts for
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eess.SY2023★ 2 cited
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Truong X. Nghiem, Ján Drgoňa, Colin Jones +10
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathema…
stat.ML2023
No-Regret Constrained Bayesian Optimization of Noisy and Expensive Hybrid Models using Differentiable Quantile Function Approximations
Congwen Lu, Joel A. Paulson
This paper investigates the problem of efficient constrained global optimization of hybrid models that are a composition of a known white-box function and an expensive multi-output…
math.OC2023
Multi-agent Black-box Optimization using a Bayesian Approach to Alternating Direction Method of Multipliers
Dinesh Krishnamoorthy, Joel A. Paulson
Bayesian optimization (BO) is a powerful black-box optimization framework that looks to efficiently learn the global optimum of an unknown system by systematically trading-off betw…