Panagiotis Petsagkourakis
Panagiotis Petsagkourakis
Sargent Centre for Process Systems Engineering
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Zitiert von
Zitiert von
Reinforcement Learning for Batch Bioprocess Optimization
P Petsagkourakis, IO Sandoval, E Bradford, D Zhang, ...
Computers & Chemical Engineering 133 (106649), 2020
Hybrid physics‐based and data‐driven modeling for bioprocess online simulation and optimization
D Zhang, EA Del Rio‐Chanona, P Petsagkourakis, J Wagner
Biotechnology and bioengineering 116 (11), 2919-2930, 2019
Real-time optimization meets Bayesian optimization and derivative-free optimization: A tale of modifier adaptation
EA del Rio Chanona, P Petsagkourakis, E Bradford, JEA Graciano, ...
Computers & Chemical Engineering 147, 107249, 2021
Constrained model-free reinforcement learning for process optimization
E Pan, P Petsagkourakis, M Mowbray, D Zhang, EA del Rio-Chanona
Computers & Chemical Engineering 154, 107462, 2021
Data-driven optimization for process systems engineering applications
D Van De Berg, T Savage, P Petsagkourakis, D Zhang, N Shah, ...
Chemical Engineering Science 248, 117135, 2022
Chance constrained policy optimization for process control and optimization
P Petsagkourakis, IO Sandoval, E Bradford, F Galvanin, D Zhang, ...
Journal of Process Control 111, 35-45, 2022
Safe chance constrained reinforcement learning for batch process control
M Mowbray, P Petsagkourakis, EA del Rio-Chanona, D Zhang
Computers & chemical engineering 157, 107630, 2022
Stability analysis of piecewise affine systems with multi-model predictive control
P Petsagkourakis, WP Heath, C Theodoropoulos
Automatica 111, 108539, 2020
Kinetic and hybrid modeling for yeast astaxanthin production under uncertainty
F Vega‐Ramon, X Zhu, TR Savage, P Petsagkourakis, K Jing, D Zhang
Biotechnology and Bioengineering 118 (12), 4854-4866, 2021
Integrating process design and control using reinforcement learning
S Sachio, M Mowbray, MM Papathanasiou, EA del Rio-Chanona, ...
Chemical Engineering Research and Design 183, 160-169, 2022
Robust stability of barrier-based model predictive control
P Petsagkourakis, WP Heath, J Carrasco, C Theodoropoulos
IEEE Transactions on Automatic Control 66 (4), 1879-1886, 2020
Data-driven distributionally robust mpc using the wasserstein metric
Z Zhong, EA del Rio-Chanona, P Petsagkourakis
arXiv preprint arXiv:2105.08414, 2021
Safe model-based design of experiments using Gaussian processes
P Petsagkourakis, F Galvanin
Computers & Chemical Engineering 151, 107339, 2021
Constrained reinforcement learning for dynamic optimization under uncertainty
P Petsagkourakis, IO Sandoval, E Bradford, D Zhang, ...
IFAC-PapersOnLine 53 (2), 11264-11270, 2020
Reinforcement learning for batch-to-batch bioprocess optimisation
P Petsagkourakis, IO Sandoval, E Bradford, D Zhang, ...
Computer Aided Chemical Engineering 46, 919-924, 2019
Tube-based distributionally robust model predictive control for nonlinear process systems via linearization
Z Zhong, EA del Rio-Chanona, P Petsagkourakis
Computers & Chemical Engineering 170, 108112, 2023
Data driven reduced order nonlinear multiparametric mpc for large scale systems
P Petsagkourakis, C Theodoropoulos
Computer Aided Chemical Engineering 43, 1249-1254, 2018
Neural odes as feedback policies for nonlinear optimal control
IO Sandoval, P Petsagkourakis, EA del Rio-Chanona
IFAC-PapersOnLine 56 (2), 4816-4821, 2023
Safe real-time optimization using multi-fidelity Gaussian processes
P Petsagkourakis, B Chachuat, EA del Rio-Chanona
2021 60th IEEE Conference on Decision and Control (CDC), 6734-6741, 2021
Simultaneous process design and control optimization using reinforcement learning
S Sachio, E Antonio, P Petsagkourakis
IFAC-PapersOnLine 54 (3), 510-515, 2021
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