On the Bayesian calibration of computer model mixtures through experimental data, and the design of predictive models

Karagiannis, G.; Lin, G.

doi:10.1016/j.jcp.2017.04.003

Recursive nearest neighbor co‐kriging models for big multi‐fidelity spatial data sets (2024)
Journal Article
Cheng, S., Konomi, B. A., Karagiannis, G., & Kang, E. L. (2024). Recursive nearest neighbor co‐kriging models for big multi‐fidelity spatial data sets. Environmetrics, 35(4), Article e2844. https://doi.org/10.1002/env.2844

Big datasets are gathered daily from different remote sensing platforms. Recently, statistical co‐kriging models, with the help of scalable techniques, have been able to combine such datasets by using spatially varying bias corrections. The associate... Read More about Recursive nearest neighbor co‐kriging models for big multi‐fidelity spatial data sets.

Toward Smart Energy Systems: The Case of Relevance Vector Regression Models in Hourly Solar Power Forecasting (2023)
Book Chapter
Alamaniotis, M., & Karagiannis, G. (2023). Toward Smart Energy Systems: The Case of Relevance Vector Regression Models in Hourly Solar Power Forecasting. In I. Hatzilygeroudis, G. Tsihrintzis, & L. Jain (Eds.), Fusion of Machine Learning Paradigms (119-127). Springer International Publishing

Ice Model Calibration using Semi-continuous Spatial Data (2022)
Journal Article
Chang, W., Konomi, B., Karagiannis, G., Guan, Y., & Haran, M. (2022). Ice Model Calibration using Semi-continuous Spatial Data. Annals of Applied Statistics, 16(3), 1937-1961. https://doi.org/10.1214/21-aoas1577

Rapid changes in Earth’s cryosphere caused by human activity can lead to significant environmental impacts. Computer models provide a useful tool for understanding the behavior and projecting the future of Arctic and Antarctic ice sheets. However, th... Read More about Ice Model Calibration using Semi-continuous Spatial Data.

Inverse Modeling of Hydrologic Parameters in CLM4 via Generalized Polynomial Chaos in the Bayesian Framework (2022)
Journal Article
Karagiannis, G., Hou, Z., Huang, M., & Lin, G. (2022). Inverse Modeling of Hydrologic Parameters in CLM4 via Generalized Polynomial Chaos in the Bayesian Framework. Computation, 10(5), Article 72. https://doi.org/10.3390/computation10050072

In this work, generalized polynomial chaos (gPC) expansion for land surface model parameter estimation is evaluated. We perform inverse modeling and compute the posterior distribution of the critical hydrological parameters that are subject to great... Read More about Inverse Modeling of Hydrologic Parameters in CLM4 via Generalized Polynomial Chaos in the Bayesian Framework.

Multifidelity computer model emulation with high‐dimensional output: An application to storm surge (2022)
Journal Article
Ma, P., Karagiannis, G., Konomi, B., Asher, T., Toro, G., & Cox, A. (2022). Multifidelity computer model emulation with high‐dimensional output: An application to storm surge. Journal of the Royal Statistical Society: Series C, 71(4), 861-883. https://doi.org/10.1111/rssc.12558

Hurricane-driven storm surge is one of the most deadly and costly natural disasters, making precise quantification of the surge hazard of great importance. Surge hazard quantification is often performed through physics-based computer models of storm... Read More about Multifidelity computer model emulation with high‐dimensional output: An application to storm surge.

Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction (2021)
Presentation / Conference Contribution
Deng, W., Feng, Q., Karagiannis, G., Lin, G., & Liang, F. (2021, December). Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction. Paper presented at International Conference on Learning Representations (ICLR'21), Virtual Event

Replica exchange stochastic gradient Langevin dynamics (reSGLD) has shown promise in accelerating the convergence in non-convex learning; however, an excessively large correction for avoiding biases from noisy energy estimators has limited the potent... Read More about Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction.

Introduction to Bayesian Statistical Inference (2021)
Book Chapter
Karagiannis, G. (2022). Introduction to Bayesian Statistical Inference. In L. Aslett, F. Coolen, & J. De Bock (Eds.), Uncertainty in Engineering: Introduction to Methods and Applications (1-13). (1). Springer Verlag. https://doi.org/10.1007/978-3-030-83640-5_1

We present basic concepts of Bayesian statistical inference. We briefly introduce the Bayesian paradigm. We present the conjugate priors; a computational convenient way to quantify prior information for tractable Bayesian statistical analysis. We pre... Read More about Introduction to Bayesian Statistical Inference.

Data Driven Update of Load Forecasts in Smart Power Systems using Fuzzy Fusion of Learning GPs (2021)
Presentation / Conference Contribution
Alamaniotis, M., Martinez-Molina, A., & Karagiannis, G. (2021). Data Driven Update of Load Forecasts in Smart Power Systems using Fuzzy Fusion of Learning GPs. . https://doi.org/10.1109/powertech46648.2021.9494757

One of the pillars in developing smart power systems is the use of load forecasting methods. In particular load forecasting accommodates decision making pertained to the operation of power market. In this paper, a new method for real-time updating ve... Read More about Data Driven Update of Load Forecasts in Smart Power Systems using Fuzzy Fusion of Learning GPs.

Hierarchical Bayesian Nearest Neighbor Co-Kriging Gaussian Process Models; An Application to Intersatellite (2021)
Journal Article
Cheng, S., Konomi, B., Matthews, J., Karagiannis, G., & Kang, E. (2021). Hierarchical Bayesian Nearest Neighbor Co-Kriging Gaussian Process Models; An Application to Intersatellite. Spatial Statistics, 44, Article 100516. https://doi.org/10.1016/j.spasta.2021.100516

Recent advancements in remote sensing technology and the increasing size of satellite constellations allow for massive geophysical information to be gathered daily on a global scale by numerous platforms of different fidelity. The auto-regressive co-... Read More about Hierarchical Bayesian Nearest Neighbor Co-Kriging Gaussian Process Models; An Application to Intersatellite.

Bayesian analysis of multifidelity computer models with local features and non-nested experimental designs: Application to the WRF model (2020)
Journal Article
Konomi, B., & Karagiannis, G. (2021). Bayesian analysis of multifidelity computer models with local features and non-nested experimental designs: Application to the WRF model. Technometrics, 63(4), 510-522. https://doi.org/10.1080/00401706.2020.1855253

Motivated by a multi-fidelity Weather Research and Forecasting (WRF) climate model application where the available simulations are not generated based on hierarchically nested experimental design, we develop a new co-kriging procedure called Augmente... Read More about Bayesian analysis of multifidelity computer models with local features and non-nested experimental designs: Application to the WRF model.

Calibrations and validations of biological models with an application on the renal fibrosis (2020)
Journal Article
Karagiannis, G., Hao, W., & Lin, G. (2020). Calibrations and validations of biological models with an application on the renal fibrosis. International Journal for Numerical Methods in Biomedical Engineering, 36(5), Article e3329. https://doi.org/10.1002/cnm.3329

We calibrate a mathematical model of renal tubulointerstitial fibrosis by Hao et al which is used to explore potential drugs for Lupus Nephritis, against a real data set of 84 patients. For this purpose, we present a general calibration procedure whi... Read More about Calibrations and validations of biological models with an application on the renal fibrosis.

Application of fuzzy multiplexing of learning Gaussian processes for the interval forecasting of wind speed (2019)
Journal Article
Alamaniotis, M., & Karagiannis, G. (2020). Application of fuzzy multiplexing of learning Gaussian processes for the interval forecasting of wind speed. IET Renewable Power Generation, 14(1), 100-109. https://doi.org/10.1049/iet-rpg.2019.0538

Robust forecasting of wind speed values is a key element to effectively accommodate renewable generation from wind in smart power systems. However, the stochastic nature of wind and the uncertainties associated with it impose high challenge in its fo... Read More about Application of fuzzy multiplexing of learning Gaussian processes for the interval forecasting of wind speed.

ELM-Fuzzy Method for Automated Decision-Making in Price Directed Electricity Markets (2019)
Presentation / Conference Contribution
Alamaniotis, M., & Karagiannis, G. (2019). ELM-Fuzzy Method for Automated Decision-Making in Price Directed Electricity Markets. . https://doi.org/10.1109/eem.2019.8916549

Among many domains application of information technologies has also transformed electricity markets. Price directed markets refer to the driving the electricity consumption by controlling the electricity prices in real time. This paper frames itself... Read More about ELM-Fuzzy Method for Automated Decision-Making in Price Directed Electricity Markets.

Minute Ahead Wind Speed Forecasting Using a Gaussian Process and Fuzzy Assimilation (2019)
Presentation / Conference Contribution
Alamaniotis, M., & Karagiannis, G. (2019). Minute Ahead Wind Speed Forecasting Using a Gaussian Process and Fuzzy Assimilation. In 2019 IEEE Milan PowerTech. https://doi.org/10.1109/ptc.2019.8810415

This paper presents an intelligent data driven method for forecasting minute ahead wind speed, which is essential in predicting the power output coming from wind generators. The proposed methodology, is based on the principle that “the most recent pa... Read More about Minute Ahead Wind Speed Forecasting Using a Gaussian Process and Fuzzy Assimilation.

Learning Uncertainty of Wind Speed Forecasting Using a Fuzzy Multiplexer of Gaussian Processes (2018)
Presentation / Conference Contribution
Alamaniotis, M., & Karagiannis, G. (2018). Learning Uncertainty of Wind Speed Forecasting Using a Fuzzy Multiplexer of Gaussian Processes. . https://doi.org/10.1049/cp.2018.1888

The smart power systems of the future will be able to accommodate wind power at a maximum efficiency by utilizing available information. For instance, information pertained to wind speed is essential in forecasting the overall amount of power generat... Read More about Learning Uncertainty of Wind Speed Forecasting Using a Fuzzy Multiplexer of Gaussian Processes.

A three-stage scheme for consumers' partitioning using hierarchical clustering algorithm (2017)
Presentation / Conference Contribution
Nasiakou, A., Alamaniotis, M., Tsoukalas, L. H., & Karagiannis, G. (2017). A three-stage scheme for consumers' partitioning using hierarchical clustering algorithm. In 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA). https://doi.org/10.1109/iisa.2017.8316375

The clustering of any type of consumers (residential, commercial, industrial) is of great importance in the operation of Smart Grids. In this paper, we propose a three-stage hierarchical scheme for residential consumers' partitioning using the Hierar... Read More about A three-stage scheme for consumers' partitioning using hierarchical clustering algorithm.

On the Bayesian calibration of expensive computer models with input dependent parameters (2017)
Journal Article
Karagiannis, G., Konomi, B., & Lin, G. (2019). On the Bayesian calibration of expensive computer models with input dependent parameters. Spatial Statistics, 34, Article 100258. https://doi.org/10.1016/j.spasta.2017.08.002

Computer models, aiming at simulating a complex real system, are often calibrated in the light of data to improve performance. Standard calibration methods assume that the optimal values of calibration parameters are invariant to the model inputs. In... Read More about On the Bayesian calibration of expensive computer models with input dependent parameters.

Integration of Gaussian Processes and Particle Swarm Optimization for Very-Short-Term Wind Speed Forecasting in Smart Power (2017)
Journal Article
Alamaniotis, M., & Karagiannis, G. (2017). Integration of Gaussian Processes and Particle Swarm Optimization for Very-Short-Term Wind Speed Forecasting in Smart Power. International Journal of Monitoring and Surveillance Technologies Research, 5(3), 1-14. https://doi.org/10.4018/ijmstr.2017070101

This article describes how the integration of renewable energy in the power grid is a critical issue in order to realize a smart grid infrastructure. To that end, intelligent methods that monitor and currently predict the values of critical variables... Read More about Integration of Gaussian Processes and Particle Swarm Optimization for Very-Short-Term Wind Speed Forecasting in Smart Power.

Bayesian Treed Calibration: an application to carbon capture with AX sorbent (2017)
Journal Article
Konomi, B., Karagiannis, G., Lai, C., & Lin, G. (2017). Bayesian Treed Calibration: an application to carbon capture with AX sorbent. Journal of the American Statistical Association, 112(517), 37-53. https://doi.org/10.1080/01621459.2016.1190279

In cases where field (or experimental) measurements are not available, computer models can model real physical or engineering systems to reproduce their outcomes. They are usually calibrated in light of experimental data to create a better representa... Read More about Bayesian Treed Calibration: an application to carbon capture with AX sorbent.

On the Bayesian calibration of computer model mixtures through experimental data, and the design of predictive models (2017)
Journal Article
Karagiannis, G., & Lin, G. (2017). On the Bayesian calibration of computer model mixtures through experimental data, and the design of predictive models. Journal of Computational Physics, 342, 139-160. https://doi.org/10.1016/j.jcp.2017.04.003

For many real systems, several computer models may exist with different physics and predictive abilities. To achieve more accurate simulations/predictions, it is desirable for these models to be properly combined and calibrated. We propose the Bayesi... Read More about On the Bayesian calibration of computer model mixtures through experimental data, and the design of predictive models.

All Outputs (28)