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ORCIDJohannes Jäschke
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2020 – today
- 2024
[i7]Evren Mert Turan, Johannes Jäschke:
Closed-loop training of static output feedback neural network controllers for large systems: A distillation case study. CoRR abs/2402.19309 (2024)- 2023
- [j11]Andrea Tuveri
, Caroline S. M. Nakama, José Matias, Haakon Eng Holck, Johannes Jäschke, Lars Imsland, Nadav Bar:
A regularized Moving Horizon Estimator for combined state and parameter estimation in a bioprocess experimental application. Comput. Chem. Eng. 172: 108183 (2023) - [j10]Simen Bjorvand, Johannes Jäschke:
Improving Primal Decomposition for Multistage MPC Using an Extended Newton Method. IEEE Control. Syst. Lett. 7: 2677-2682 (2023) - [i6]Zawadi Mdoe, Dinesh Krishnamoorthy, Johannes Jäschke:
Stability Properties of the Adaptive Horizon Multi-Stage MPC. CoRR abs/2305.19448 (2023) - [i5]Carine Menezes Rebello, Johannes Jäschke, Idelfonso B. R. Nogueira:
Digital Twin Framework for Optimal and Autonomous Decision-Making in Cyber-Physical Systems: Enhancing Reliability and Adaptability in the Oil and Gas Industry. CoRR abs/2311.12755 (2023) - [i4]Evren Mert Turan, Zawadi Mdoe, Johannes Jäschke:
Learning convex objectives to reduce the complexity of model predictive control. CoRR abs/2312.02650 (2023) - 2022
- [j9]Evren Mert Turan, Johannes Jäschke:
Multiple Shooting for Training Neural Differential Equations on Time Series. IEEE Control. Syst. Lett. 6: 1897-1902 (2022) - [j8]Xingheng Liu, José Matias, Johannes Jäschke, Jørn Vatn:
Gibbs sampler for noisy Transformed Gamma process: Inference and remaining useful life estimation. Reliab. Eng. Syst. Saf. 217: 108084 (2022) - [c7]Vinh Phuc Bui Nguyen, José Matias, Johannes Jäschke:
Convolutional Neural Network as a Steady-state Detector for Real-time Optimization. ECC 2022: 2247-2252 - [i3]Timur Bikmukhametov, Johannes Jäschke:
Hybrid Machine Learning Modeling of Engineering Systems - A Probabilistic Perspective Tested on a Multiphase Flow Modeling Case Study. CoRR abs/2205.09196 (2022) - 2021
- [j7]José Matias, Johannes Jäschke:
Online model maintenance in real-time optimization methods. Comput. Chem. Eng. 145: 107141 (2021) - [j6]Mandar Thombre, Zhou (Joyce) Yu, Johannes Jäschke, Lorenz T. Biegler:
Sensitivity-Assisted multistage nonlinear model predictive control: Robustness, stability and computational efficiency. Comput. Chem. Eng. 148: 107269 (2021) - [c6]Zawadi Mdoe, Dinesh Krishnamoorthy, Johannes Jäschke:
Adaptive Horizon Multistage Nonlinear Model Predictive Control. ACC 2021: 2088-2093 - [i2]José Matias, Julio P. C. Oliveira, Galo A. C. Le Roux, Johannes Jäschke:
Steady-state Real-time Optimization Using Transient Measurements on an Experimental Rig. CoRR abs/2109.00795 (2021) - [i1]Evren Mert Turan, Johannes Jäschke:
Multiple shooting with neural differential equations. CoRR abs/2109.06786 (2021) - 2020
- [j5]Timur Bikmukhametov, Johannes Jäschke:
Combining machine learning and process engineering physics towards enhanced accuracy and explainability of data-driven models. Comput. Chem. Eng. 138: 106834 (2020)
2010 – 2019
- 2019
- [c5]Krishnamoorthy Dinesh, Sigurd Skogestad, Johannes Jäschke:
Multistage Model Predictive Control with Online Scenario Tree Update using Recursive Bayesian Weighting. ECC 2019: 1443-1448 - 2018
- [j4]Krishnamoorthy Dinesh, Eka Suwartadi, Bjarne Foss, Sigurd Skogestad, Johannes Jäschke:
Improving Scenario Decomposition for Multistage MPC Using a Sensitivity-Based Path-Following Algorithm. IEEE Control. Syst. Lett. 2(4): 581-586 (2018) - 2017
- [j3]Johannes Jäschke, Yi Cao, Vinay Kariwala:
Self-optimizing control - A survey. Annu. Rev. Control. 43: 199-223 (2017) - [j2]Vyacheslav Kungurtsev, Johannes Jäschke:
A Predictor-Corrector Path-Following Algorithm for Dual-Degenerate Parametric Optimization Problems. SIAM J. Optim. 27(1): 538-564 (2017) - [c4]Tamal Das, Johannes Jäschke:
Pathfollowing approach for a sensitivity based moving horizon estimator. CDC 2017: 3021-3026 - 2014
- [j1]Johannes Jäschke, Sigurd Skogestad:
Optimal operation of heat exchanger networks with stream split: Only temperature measurements are required. Comput. Chem. Eng. 70: 35-49 (2014) - 2011
- [c3]Johannes Jäschke, Miroslav Fikar, Sigurd Skogestad:
Self-optimizing invariants in dynamic optimization. CDC/ECC 2011: 7753-7758
2000 – 2009
- 2009
- [c2]Johannes Jäschke, Helge Smedsrud, Sigurd Skogestad, Henrik Manum:
Optimal operation of a waste incineration plant for district heating. ACC 2009: 665-670 - [c1]Henrik Manum, Sigurd Skogestad, Johannes Jäschke:
Convex initialization of the H2-optimal static output feedback problem. ACC 2009: 1724-1729
Coauthor Index
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