Gaussian process based optimization algorithms with input uncertainty

Wang, Haowei; Yuan, Jun; Ng, Szu Hui

doi:10.1080/24725854.2019.1639859

Cited by 23 publications

(19 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can be seen that the predicted values were close to the observed values in most cases for all three models. Given the network with the best structure, the performance of the BPNN, without taking into uncertainty, and the BNN, considering uncertainty, were compared with the GP model proposed in [39]. Figures 3-5 show the fit of predicted and observed fuel consumption for three models, using training data and validation data.…”

Section: Resultsmentioning

confidence: 99%

“…Also, the performance of the BPNN and BNN with the best structure was further compared with the GP model, which made it easy to account for various uncertainties [16]. The GP model proposed by [39] was applied here. The 95% confidence interval of prediction for three models was also computed, and the probability that the observed fuel consumption was within the 95% confidence interval of predicted fuel consumption could be obtained.…”

Section: Mitigation Potential Evaluation Using Bpnn and Bnnmentioning

confidence: 99%

See 1 more Smart Citation

Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures

2020

Self Cite

View full text Add to dashboard Cite

Climate change caused by greenhouse gas emissions is of critical concern to international shipping. A large portfolio of mitigation measures has been developed to mitigate ship gas emissions by reducing ship energy consumption but is constrained by practical considerations, especially cost. There are difficulties in ranking the priority of mitigation measures, due to the uncertainty of ship information and data gathered from onboard instruments and other sources. In response, a neural network model is proposed to evaluate the cost-effectiveness of mitigation measures based on decarbonization. The neural network is further enhanced with a Bayesian method to consider the uncertainties of model parameters. Three of the key advantages of the proposed approach are (i) its ability to simultaneously consider a wide range of sources of information and data that can help improve the robustness of the modeling results; (ii) the ability to take into account the input uncertainties in ranking and selection; (iii) the ability to include marginal costs in evaluating the cost-effectiveness of mitigation measures to facilitate decision making. In brief, a negative “marginal cost-effectiveness” would indicate a priority consideration for a given mitigation measure. In the case study, it was found that weather routing and draft optimization could have negative marginal cost-effectiveness, signaling the importance of prioritizing these measures.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Mitigation Potential Evaluation Using Bpnn and Bnnmentioning

confidence: 99%

Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…There are some GP-based methods specifically considering intrinsic (input) uncertainty. Wang et al (2019) refined the GP-based optimization algorithms to solve the stochastic simulation optimization problems considering input uncertainty. investigated GP regression considering the input location error within FEA simulations.…”

Section: Review On Uncertainty Quantification and Stochastic Surrogatesmentioning

confidence: 99%

NP-ODE: Neural process aided ordinary differential equations for uncertainty quantification of finite element analysis

Wang

Cai

et al. 2021

IISE Transactions

View full text Add to dashboard Cite

Finite element analysis (FEA) has been widely used to generate simulations of complex and nonlinear systems. Despite its strength and accuracy, the limitations of FEA can be summarized into two aspects: a) running high-fidelity FEA often requires significant computational cost and consumes a large amount of time; b) FEA is a deterministic method which is insufficient for uncertainty quantification (UQ) when modeling complex systems with various types of uncertainties. In this paper, an physics-informed data-driven surrogate model, named Neural Process Aided Ordinary Differential Equation (NP-ODE), is proposed to model the FEA simulations and capture both input and output uncertainties. To validate the advantages of the proposed NP-ODE, we conduct experiments on both the simulation data generated from a given ordinary differential equation and the data collected from a real FEA platform for tribocorrosion.The performances of the proposed NP-ODE and several benchmark methods are compared. The results show that the proposed NP-ODE outperforms benchmark methods. The NP-ODE method realizes the smallest predictive error as well as generates the most reasonable confidence interval having the best coverage on testing data points.

show abstract

“…Other related works include the literature on nested simulation, e.g. Lan, Nelson, and Staum (2010), , Broadie, Du, and Moallemi (2015), H. ; Jaiswal, Honnappa, and Rao (2019), which studies the data-driven risk averse optimization problem under a parameterized Bayesian setting using the log-exponential risk measure; and H. Wang, Yuan, and Ng (2020), which uses Bayesian Optimization (see Frazier, 2018) methods to optimize the expectation case of BRO with black-box expensive-to-evaluate objective functions.…”

Section: Introductionmentioning

confidence: 99%

Solving Bayesian Risk Optimization via Nested Stochastic Gradient Estimation

Cakmak

Zhou

2020

Preprint

View full text Add to dashboard Cite

In this paper, we aim to solve Bayesian Risk Optimization (BRO), which is a recently proposed framework that formulates simulation optimization under input uncertainty. In order to efficiently solve the BRO problem, we derive nested stochastic gradient estimators and propose corresponding stochastic approximation algorithms. We show that our gradient estimators are asymptotically unbiased and consistent, and that the algorithms converge asymptotically. We demonstrate the empirical performance of the algorithms on a two-sided market model. Our estimators are of independent interest in extending the literature of stochastic gradient estimation to the case of nested risk functions.

show abstract

Gaussian process based optimization algorithms with input uncertainty

Cited by 23 publications

References 50 publications

Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures

Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures

NP-ODE: Neural process aided ordinary differential equations for uncertainty quantification of finite element analysis

Solving Bayesian Risk Optimization via Nested Stochastic Gradient Estimation

Contact Info

Product

Resources

About