| Multiple shooting for training neural differential equations on time series EM Turan, J Jäschke IEEE Control Systems Letters 6, 1897-1902, 2021 | 34 | 2021 |
| Classification of undesirable events in oil well operation EM Turan, J Jäschke 2021 23rd international conference on process control (PC), 157-162, 2021 | 24 | 2021 |
| Closed-loop optimisation of neural networks for the design of feedback policies under uncertainty EM Turan, J Jäschke Journal of Process Control 133, 103144, 2024 | 8 | 2024 |
| A flow sheet for the conversion of lunar regolith using fluorine gas EM Turan, SA Stein, R Maharaj, KP Möller Advances in Space Research 65 (7), 1852-1862, 2020 | 8 | 2020 |
| Comparison of mechanisms for the direct, gas phase, partial oxidation of methane to methanol EM Turan, E van Steen, KP Möller Chemical Engineering Science 241, 116718, 2021 | 7 | 2021 |
| Designing neural network control policies under parametric uncertainty: A Koopman operator approach EM Turan, J Jäschke IFAC-PapersOnLine 55 (7), 392-399, 2022 | 4 | 2022 |
| Design of PID controllers using semi-infinite programming EM Turan, R Kannan, J Jäschke Computer Aided Chemical Engineering 49, 439-444, 2022 | 2 | 2022 |
| Closed-loop training of static output feedback neural network controllers for large systems: A distillation case study EM Turan, J Jäschke Journal of Process Control 143, 103302, 2024 | 1 | 2024 |
| Learning a convex cost-to-go for single step model predictive control EM Turan, Z Mdoe, J Jäschke arXiv preprint arXiv:2312.02650, 2023 | 1 | 2023 |
| A simple two-parameter steady-state detection algorithm: Concept and experimental validation EM Turan, J Jäschke Computer Aided Chemical Engineering 52, 1765-1770, 2023 | 1 | 2023 |
| Model Predictive Control for Bottleneck Isolation with Unmeasured Faults EM Turan, S Skogestad, J Jäschke IFAC-PapersOnLine 58 (14), 767-774, 2024 | | 2024 |
| Advances in Optimisation and Machine Learning for Process Systems Engineering EM Turan NTNU, 2024 | | 2024 |
| Optimality-Based Discretization Methods for the Global Optimization of Nonconvex Semi-Infinite Programs EM Turan, J Jäschke, R Kannan arXiv preprint arXiv:2303.00219, 2023 | | 2023 |
| Learning convex objectives to reduce the complexity of model predictive control. EM Turan, Z Mdoe, J Jäschke CoRR, 2023 | | 2023 |
| Experimental validation of modifier adaptation and Gaussian processes for real time optimisation EM Turan, S Lia, J Matias, J Jäschke IFAC-PapersOnLine 56 (2), 1394-1399, 2023 | | 2023 |
| Online Steady and Transient State Detection Using the Dickey-Fuller Test E Turan, J Jäschke 2022 AIChE Annual Meeting, 2022 | | 2022 |
| Tighter Lower Bounds for Semi-Infinite Programming Using Parametric Sensitivity Theory E Turan, J Jäschke, R Kannan 2022 AIChE Annual Meeting, 2022 | | 2022 |
| Scientific Machine Learning: Tuning out the Noise FB Konow, J Jäschke, E Turan | | 2021 |
| Multiple shooting with neural differential equations EM Turan, J Jäschke system 3, 4, 2021 | | 2021 |
Johannes JäschkeProfessor, Department Chemical Engineering, Norwegian University of Science andBestätigte E-Mail-Adresse bei ntnu.no
