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Journal Articles
- 2021
[j7]Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis
, Eric Bradford, Jose Eduardo Alves Graciano, Benoît Chachuat:
Real-time optimization meets Bayesian optimization and derivative-free optimization: A tale of modifier adaptation. Comput. Chem. Eng. 147: 107249 (2021)- 2020
- [j6]Panagiotis Petsagkourakis, Ilya Orson Sandoval, Eric Bradford, Dongda Zhang, Ehecatl Antonio del Rio-Chanona:
Reinforcement learning for batch bioprocess optimization. Comput. Chem. Eng. 133 (2020) - [j5]Eric Bradford, Lars Imsland, Dongda Zhang, Ehecatl Antonio del Rio-Chanona:
Stochastic data-driven model predictive control using gaussian processes. Comput. Chem. Eng. 139: 106844 (2020) - 2019
- [j4]Eric Bradford, Lars Imsland:
Output feedback stochastic nonlinear model predictive control for batch processes. Comput. Chem. Eng. 126: 434-450 (2019) - 2018
- [j3]Eric Bradford, Artur M. Schweidtmann, Dongda Zhang, Keju Jing, Ehecatl Antonio del Rio-Chanona:
Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes. Comput. Chem. Eng. 118: 143-158 (2018) - [j2]Eric Bradford, Artur M. Schweidtmann, Alexei A. Lapkin:
Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm. J. Glob. Optim. 71(2): 407-438 (2018) - [j1]Eric Bradford, Artur M. Schweidtmann, Alexei A. Lapkin:
Correction to: Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm. J. Glob. Optim. 71(2): 439-440 (2018)
Conference and Workshop Papers
- 2020
- [c4]Leif Erik Andersson, Eric Christopher Bradford, Lars Imsland:
Distributed learning for wind farm optimization with Gaussian processes. ACC 2020: 4058-4064 - 2019
- [c3]Eric Bradford, Lars Imsland, Ehecatl Antonio del Rio-Chanona:
Nonlinear model predictive control with explicit back-offs for Gaussian process state space models. CDC 2019: 4747-4754 - [c2]Eric Bradford, Marcus Reble, Lars Imsland:
Output feedback stochastic nonlinear model predictive control of a polymerization batch process. ECC 2019: 3144-3151 - 2018
- [c1]Eric Bradford, Lars Imsland:
Stochastic Nonlinear Model Predictive Control Using Gaussian Processes. ECC 2018: 1027-1034
Informal and Other Publications
- 2021
- [i6]Eric Bradford, Lars Imsland, Marcus Reble, Ehecatl Antonio del Rio-Chanona:
Hybrid Gaussian Process Modeling Applied to Economic Stochastic Model Predictive Control of Batch Processes. CoRR abs/2108.06430 (2021) - 2020
- [i5]Panagiotis Petsagkourakis, Ilya Orson Sandoval, Eric Bradford, Dongda Zhang, Ehecatl Antonio del Rio-Chanona:
Constrained Reinforcement Learning for Dynamic Optimization under Uncertainty. CoRR abs/2006.02750 (2020) - [i4]Panagiotis Petsagkourakis, Ilya Orson Sandoval, Eric Bradford, Federico Galvanin, Dongda Zhang, Ehecatl Antonio del Rio-Chanona:
Chance Constrained Policy Optimization for Process Control and Optimization. CoRR abs/2008.00030 (2020) - [i3]Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis, Eric Bradford, Jose Eduardo Alves Graciano, Benoît Chachuat:
Modifier Adaptation Meets Bayesian Optimization and Derivative-Free Optimization. CoRR abs/2009.08819 (2020) - 2019
- [i2]Panagiotis Petsagkourakis, Ilya Orson Sandoval, Eric Bradford, Dongda Zhang, Ehecatl Antonio del Rio-Chanona:
Reinforcement Learning for Batch Bioprocess Optimization. CoRR abs/1904.07292 (2019) - [i1]Eric Bradford, Lars Imsland, Dongda Zhang, Ehecatl Antonio del Rio-Chanona:
Stochastic data-driven model predictive control using Gaussian processes. CoRR abs/1908.01786 (2019)
Coauthor Index
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