Reference author: Grigoriy Kimaev

Kimaev, G., & Ricardez-Sandoval, L. A. (2019). Multilevel Monte Carlo sampling for thin film deposition under uncertainty Presented at the 2019 AIChE Annual Meeting conference. Retrieved from https://aiche.confex.com/aiche/2019/meetingapp.cgi/Paper/575431 (Original work published 2019)

Kimaev, G., & Ricardez-Sandoval, L. A. (2019). Nonlinear model predictive control of a multiscale thin film deposition process using artificial neural networks Chemical Engineering Science, 207. Retrieved from https://www.sciencedirect.com/science/article/pii/S0009250919306086 (Original work published 2019)

Kimaev, G., & Ricardez-Sandoval, L. A. (2019). Robust Artificial Neural Networks for Nonlinear Model Predictive Control of Multiscale Stochastic Systems Presented at the 2019 AIChE Annual Meeting conference. Retrieved from https://aiche.confex.com/aiche/2019/meetingapp.cgi/Paper/575528 (Original work published 2019)

Kimaev, G., & Ricardez-Sandoval, L. A. (2018). Multilevel Monte Carlo Applied for Efficient Estimation of Observables in Multiscale Stochastic Systems Presented at the 2018 AIChE Annual Meeting conference. (Original work published 2018)

Kimaev, G., & Ricardez-Sandoval, L. A. (2018). Multilevel Monte Carlo applied to chemical engineering systems subject to uncertainty AIChE Journal, 64, 1651-1661. https://doi.org/10.1002/aic.16045

Kimaev, G., & Ricardez-Sandoval, L. A. (2018). Multilevel Monte Carlo for noise estimation in stochastic multiscale systems Chemical Engineering Research and Design, 140. Retrieved from https://www.sciencedirect.com/science/article/pii/S0263876218305288 (Original work published 2018)

Kimaev, G., & Ricardez-Sandoval, L. A. (2017). A comparison of efficient uncertainty quantification techniques for stochastic multiscale systems AIChE Journal, 63, 3361-3373. https://doi.org/10.1002/aic.15702

Kimaev, G., & Ricardez-Sandoval, L. A. (2020). Artificial neural network discrimination for parameter estimation and optimal product design of thin films manufactured by chemical vapor deposition The Journal of Physical Chemistry C, 124. Retrieved from https://pubs.acs.org/doi/full/10.1021/acs.jpcc.0c05250 (Original work published 2020)

Kimaev, G., & Ricardez-Sandoval, L. A. (2020). Artificial neural networks for dynamic optimization of stochastic multiscale systems subject to uncertainty Chemical Engineering Research and Design, 161. Retrieved from https://www.sciencedirect.com/science/article/pii/S0263876220302823 (Original work published 2020)

Kimaev, G., Chaffart, D., & Ricardez-Sandoval, L. A. (2020). Multilevel Monte Carlo applied for uncertainty quantification in stochastic multiscale systems AIChE Journal, 66. Retrieved from https://aiche.onlinelibrary.wiley.com/doi/full/10.1002/aic.16262 (Original work published 2020)