| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
Accelerated Process Optimization of Chromatographic Separations Using a Hybrid Modeling Approach
|
Michalopoulou, Foteini
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59 |
6 |
p. 427-432
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artikel
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| 2 |
Adaptive Optimal Control of Lettuce Growth in Greenhouses Using Sensitivity-Driven Measurement Collection
|
Valábek, Patrik
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59 |
6 |
p. 516-521
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artikel
|
| 3 |
A Data Driven Approach for Resolving Time-dependent Differential Equations with Noise ⁎ ⁎ The work is partially supported by grants from eSSENCE no. 138227, FORMAS no. 2022-151862 and AgTech Sweden. The computations were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS), partially funded by the Swedish Research Council through grant agreement no. 2022-06725.
|
Liu, Donglin
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59 |
6 |
p. 379-384
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artikel
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| 4 |
A digital tool for the automatic identification of anomalous cell cultures in biopharmaceutical process development
|
Barberi, G.
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59 |
6 |
p. 546-551
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artikel
|
| 5 |
A flow rate soft sensor for pumps with complex characteristics
|
Leonow, Sebastian
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59 |
6 |
p. 361-366
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artikel
|
| 6 |
A hierarchical multimode dynamic process monitoring scheme and its application to the Tennessee Eastman process ⁎ ⁎ This work was supported partially by Innovation and Technology Commission (ITC) and partially by Guangdong-Hong Kong Technology Cooperation Funding Scheme (Project No. GHP/145/20).
|
Wang, Jiaorao
|
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59 |
6 |
p. 217-222
|
artikel
|
| 7 |
Analysing Control-theoretic Properties of Nonlinear Synthetic Biology Circuits ⁎ ⁎ This work has been supported by grant PID2020-113992RA-I00 funded by MCIN/AEI/10.13039/501100011033, grant PID2023-146275NB-C21 funded by MICIU/AEI/10.13039/501100011033 and ERDF/EU, and grant CNS2023-144886 funded by MICIU/AEI /10.13039/501100011033 and the European Union NextGenerationEU/PRTR.
|
Pardo, Antón
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59 |
6 |
p. 7-12
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artikel
|
| 8 |
A New Generic Mass Balance Model with Multi-Layer Perceptron-Based Kinetics and Stoichiometry
|
Bogaerts, Ph.
|
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59 |
6 |
p. 283-288
|
artikel
|
| 9 |
An Integrated Optimization Method for Heavy Haul Trains with Virtual Coupling Based on Genetic Algorithm
|
Wu, Lezhou
|
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59 |
6 |
p. 606-611
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artikel
|
| 10 |
A nonlinear approach for input signal design with persistent excitation applied to pH modelling in microalgae raceway reactors ⁎ ⁎ Project co-funded by the European Union – Next Generation Eu - under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 1 Investment 4.1 - Call for tender No. 2333 (22nd December 2023) of Italian Ministry of University and Research; Concession Decree No. 118 (2nd March 2023) adopted by the Italian Ministry of University and Research, Project code D93C23000450005, within the Italian National Program PhD Programme DAuSy; Spanish Ministry of Science: grant PID2023-150739OB-I00.
|
Campregher, F.
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59 |
6 |
p. 103-108
|
artikel
|
| 11 |
A Non-linear PI Averaging Level Controller for Plantwide Systems
|
Gupta, Aayush
|
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59 |
6 |
p. 564-569
|
artikel
|
| 12 |
Application of pqEDMD for modeling open raceway ponds
|
Garcia-Tenorio, Camilo
|
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59 |
6 |
p. 403-408
|
artikel
|
| 13 |
A Reinforcement Learning Approach for Simultaneous Generation, Design and Control of Reaction-Separation Process Flowsheets
|
Reynoso-Donzelli, Simone
|
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59 |
6 |
p. 247-252
|
artikel
|
| 14 |
Automatic design of robust model predictive control of a bioreactor via Bayesian optimization ⁎ ⁎ The research leading to these results has received funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under grant agreement number 423857295.
|
Brockhoff, Tobias
|
|
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59 |
6 |
p. 19-24
|
artikel
|
| 15 |
Autonomous Industrial Control using an Agentic Framework with Large Language Models
|
Vyas, Javal
|
|
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59 |
6 |
p. 349-354
|
artikel
|
| 16 |
A wavelet neural network assisted framework for active fault detection and diagnosis of process systems
|
Hambarde, Piyush Dasrao
|
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59 |
6 |
p. 151-156
|
artikel
|
| 17 |
A Zonotope-based Big Data-driven Predictive Control Approach for Nonlinear Processes ⁎ ⁎ This work was supported by the Australian Research Council under Grant DP240100300. Corresponding author: Jie Bao.
|
Han, Shuangyu
|
|
|
59 |
6 |
p. 31-36
|
artikel
|
| 18 |
Batch-to-batch optimization of an industrial reactor using modifier adaptation
|
Aboelnour, Mohamed
|
|
|
59 |
6 |
p. 337-342
|
artikel
|
| 19 |
Benchmarking of Multi-agent Reinforcement Learning Strategies for Optimizing Cutting Plane Selection ⁎ ⁎ The authors sincerely acknowledge the funding received from DST-SERB India with file number CRG/2022/003722.
|
M., Arjun
|
|
|
59 |
6 |
p. 169-174
|
artikel
|
| 20 |
Bilevel Optimisation for Targeted Metabolic Network Reduction
|
Monteiro, Mariana
|
|
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59 |
6 |
p. 271-276
|
artikel
|
| 21 |
Blending Physics and Data to Model Hemodynamic Effects Under General Anesthesia
|
Fregolent, Mattia
|
|
|
59 |
6 |
p. 79-84
|
artikel
|
| 22 |
Combining hybrid modelling and transfer learning to simulate fed-batch bioprocess under uncertainty
|
Pennington, Oliver
|
|
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59 |
6 |
p. 49-54
|
artikel
|
| 23 |
Comparative Analysis of Control Structures in Core Annular Flow Systems: A CFD Simulation Study
|
Lima, Patrick Souza
|
|
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59 |
6 |
p. 97-102
|
artikel
|
| 24 |
Contents
|
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59 |
6 |
p. i-vii
|
artikel
|
| 25 |
Controlling Paracetamol Batch Crystallization in Ethanol by Reinforcement Learning
|
Lima, Fernando Arrais R.D.
|
|
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59 |
6 |
p. 373-378
|
artikel
|
| 26 |
Control structures for heat delivery in compact bottoming cycles for heat and power production ⁎ ⁎ This publication has been produced with support from the LowE-mission Research Centre (www.lowemission.no), performed under the Norwegian research program PETROSENTER. The authors acknowledge the industry partners in LowEmission for their contributions and the Research Council of Norway (296207).
|
Bernardino, Lucas F.
|
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59 |
6 |
p. 570-575
|
artikel
|
| 27 |
Data-driven material removal rate estimation in bonnet polishing process
|
Darowski, Michal
|
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59 |
6 |
p. 211-216
|
artikel
|
| 28 |
Decentralized causal-based monitoring for large-scale systems: sensitivity and robustness assessment
|
Paredes, Rodrigo
|
|
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59 |
6 |
p. 127-132
|
artikel
|
| 29 |
Dynamic Optimization of Molecular Weight Distribution in Industrial Batch Polymerization ⁎ ⁎ This work is supported by the Research Funds of Institute of Zhejiang University-Quzhou (IZQ2022KYZX12, IZQ2022KYZX08, IZQ2022 KJ3001, IZQ2022KJ1003) and key projects of Zhejiang High-end Chemical Technology Innovation Center (ACTIC-2022-013).
|
Lan, Xinmiao
|
|
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59 |
6 |
p. 355-360
|
artikel
|
| 30 |
Early fault diagnosis in chemical processes through multistep multivariable prediction
|
Chrysler J., Djogap F.
|
|
|
59 |
6 |
p. 409-414
|
artikel
|
| 31 |
Economic data-enabled predictive control using machine learning ⁎ ⁎ This research is supported by PUB, Singapore’s National Water Agency under its RIE2025 Urban Solutions and Sustainability (USS) (Water) Centre of Excellence (CoE) Programme, awarded to Nanyang Environment & Water Research Institute (NEWRI), Nanyang Technological University, Singapore (NTU), and Ministry of Education, Singapore, under its Academic Research Fund Tier 1 (RG63/22). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore and PUB, Singapore’s National Water Agency.
|
Yan, Mingxue
|
|
|
59 |
6 |
p. 25-30
|
artikel
|
| 32 |
Enhancing purple non-sulfur bacteria modeling with physics-informed neural networks
|
Nunes, Matheus C.R.
|
|
|
59 |
6 |
p. 55-60
|
artikel
|
| 33 |
Enhancing reinforcement learning for population setpoint tracking in co-cultures
|
Espinel-Ríos, Sebastián
|
|
|
59 |
6 |
p. 61-66
|
artikel
|
| 34 |
Estimating Growth Rate from Sparse and Noisy Data: A Bayesian Approach
|
Andersson, David
|
|
|
59 |
6 |
p. 223-228
|
artikel
|
| 35 |
Evaluating Demand Response Particibility Potential of Process Systems using Levelized Cost Analysis
|
Liu, Yu
|
|
|
59 |
6 |
p. 253-258
|
artikel
|
| 36 |
Evaporation rate independent state estimation for a spray drying process
|
Lepsien, Arthur
|
|
|
59 |
6 |
p. 415-420
|
artikel
|
| 37 |
Experimental Design for Missing Physics
|
Strouwen, Arno
|
|
|
59 |
6 |
p. 481-486
|
artikel
|
| 38 |
Fast Startup Dynamics of Diabatic Distillation with Electric Heating
|
Mercer, Samuel
|
|
|
59 |
6 |
p. 588-593
|
artikel
|
| 39 |
Fault Detection and Diagnosis using Reconstruction-based DiGLPP: Application to Industrial Distillation System
|
Ali, Husnain
|
|
|
59 |
6 |
p. 187-192
|
artikel
|
| 40 |
Fault Diagnosis For Drilling using a Multitask Physics-Informed Neural Network
|
Gkionis, Marios
|
|
|
59 |
6 |
p. 463-468
|
artikel
|
| 41 |
Generalizability of Concept Knowledge in Machine Learning Using TCAV Scores: A Case Study Using Different Skin-Lesion Datasets
|
Schwinghammer, Moritz C.
|
|
|
59 |
6 |
p. 397-402
|
artikel
|
| 42 |
Generic model control of diffusion-reaction systems
|
Maidi, A.
|
|
|
59 |
6 |
p. 558-563
|
artikel
|
| 43 |
Heterogeneous Transfer Learning from Batch to Continuous Direct Compression Tablet Manufacturing
|
Kobayashi, Yuki
|
|
|
59 |
6 |
p. 325-330
|
artikel
|
| 44 |
Hierarchical RL-MPC for Demand Response Scheduling ⁎ ⁎ M Bloor acknowledges funding provided by the Engineering & Physical Sciences Research Council, United Kingdom, through grant code EP/W524323/1. C Tsay acknowledges support from a BASF/Royal Academy of Engineering Senior Research Fellowship.
|
Bloor, Maximilian
|
|
|
59 |
6 |
p. 229-234
|
artikel
|
| 45 |
How Dynamics Improve Fault Detection: A Gaussian LTI Case
|
Gao, Xinrui
|
|
|
59 |
6 |
p. 121-126
|
artikel
|
| 46 |
Hybrid Deep Reinforcement Learning Agent for Online Scheduling and Control for Chemical Batch Plants
|
Rangel-Martinez, Daniel
|
|
|
59 |
6 |
p. 259-264
|
artikel
|
| 47 |
Hybrid Optimization Methods for Parameter Estimation of Reactive Transport Systems
|
Schytt, Marcus Johan
|
|
|
59 |
6 |
p. 163-168
|
artikel
|
| 48 |
Identifying Drivers of Downstream Yield Variability Using Integrated Process Models: An Application to API Manufacturing
|
Overgaard, Tobias
|
|
|
59 |
6 |
p. 576-581
|
artikel
|
| 49 |
iGFA: Improved Glycosylation Flux Analysis
|
Venkatesan, Shriramprasad
|
|
|
59 |
6 |
p. 265-270
|
artikel
|
| 50 |
Image-based Battery Health Monitoring for Capacity Degradation Analysis
|
Yun, Ji Young
|
|
|
59 |
6 |
p. 193-198
|
artikel
|
| 51 |
Improved Model Order Selection in Dynamical System Identification Based on Trend Extraction
|
Liu, Ju
|
|
|
59 |
6 |
p. 487-492
|
artikel
|
| 52 |
Improved Stiction Detection via Hybrid Residual Embedded Inception Module Networks
|
Aftab, Muhammad Faisal
|
|
|
59 |
6 |
p. 145-150
|
artikel
|
| 53 |
Improved Understanding of Experimental Campaigns in Catalyst Development through machine learning
|
Tamiazzo, Edoardo
|
|
|
59 |
6 |
p. 594-599
|
artikel
|
| 54 |
Improving Process Monitoring via Dynamic Multi-Fidelity Modeling
|
Fáber, Rastislav
|
|
|
59 |
6 |
p. 199-204
|
artikel
|
| 55 |
Learning Approximate Symbolic Solutions to Burgers’ Equation using Symbolic Regression ⁎ ⁎ This project was sponsored by the Pratt & Whitney Institute of Advanced Systems Engineering (P&W-IASE) of the University of Connecticut and Pratt & Whitney; and the National Institutes of Health [NIH P42-ES027704].
|
Cohen, Benjamin G.
|
|
|
59 |
6 |
p. 301-306
|
artikel
|
| 56 |
Linear-Quadratic Model Predictive Control for Continuous-time Systems with Time Delays and Piecewise Constant Inputs
|
Zhang, Zhanhao
|
|
|
59 |
6 |
p. 37-42
|
artikel
|
| 57 |
LSTM-Based Hybrid Modeling Approach for Control Application of Evaporator Involving Phase Transition
|
Byun, Jisung
|
|
|
59 |
6 |
p. 307-312
|
artikel
|
| 58 |
Machine Learning-Driven Optimisation of Operational Spaces for Uncertainty Management in Process Industries ⁎ ⁎ This project is partially supported by the EPSRC Grant of Artificial Intelligence Enabling Future Optimal Flexible Biogas Production for Net-Zero (EP/Y005600/1)
|
Kay, Sam
|
|
|
59 |
6 |
p. 241-246
|
artikel
|
| 59 |
Metabolic Modeling of Arthrospira sp. PCC 8005 - Network Definition and Experimental Validation
|
Maton, M.
|
|
|
59 |
6 |
p. 391-396
|
artikel
|
| 60 |
Model-Based Analysis of Membrane-Enhanced Liquid-Phase Oligonucleotide Synthesis ⁎ ⁎ This work was supported by Innovate UK under grant number 10103756 (Sustainable bioprocess for oligonucleotide manufacture using Nanostar Sieving – BioNanostar)
|
Saccardo, Alberto
|
|
|
59 |
6 |
p. 277-282
|
artikel
|
| 61 |
Model-based protein estimation during dough formation in a Farinograph
|
Schaum, A.
|
|
|
59 |
6 |
p. 295-300
|
artikel
|
| 62 |
Modeling of Biodiesel Production via Transesterification using Inline Raman Spectroscopy ⁎ ⁎ The authors gratefully acknowledge the financial support of the Kopernikus project SynErgie by the Federal Ministry of Education and Research (BMBF) and the project supervision by the project management organization Projek-tträger Jülich.
|
Bouchkira, Ilias
|
|
|
59 |
6 |
p. 600-605
|
artikel
|
| 63 |
Modelling and simulation of a trickling filter bioreactor for ex-situ hydrogenotrophic methanation ⁎ ⁎ Work supported by UNAM Postdoctoral Program (POSDOC); project IIUNAM-GII-3406; CONAHCYT frontier science grant CF-2023-I-537; and Mexican Researchers Program (ID 6407, project 265)
|
Ortiz-Ricárdez, F.A.
|
|
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59 |
6 |
p. 445-450
|
artikel
|
| 64 |
Modular Surrogate Models for Simulating the Amine Scrubbing Process
|
Rendall, Ricardo
|
|
|
59 |
6 |
p. 319-324
|
artikel
|
| 65 |
MORSE: An Adaptive Decision-Making Framework Combining Reinforcement Learning and Multi-Objective Evolutionary Algorithms for Dynamic Inventory Control
|
Kotecha, Niki
|
|
|
59 |
6 |
p. 235-240
|
artikel
|
| 66 |
Nonasymptotic E-Optimal Design of Experiments for System Identification Using Sign-Perturbed Sums ⁎ ⁎ This work was supported by JSPS KAKENHI, Grant Numbers JP22K04816 and JP24KJ1369.
|
Oshima, Masanori
|
|
|
59 |
6 |
p. 493-498
|
artikel
|
| 67 |
Nonlinear Model Predictive Control for Dynamic Operation of an Alkaline Electrolyzer
|
Damm Christensen, Anders Hilmar
|
|
|
59 |
6 |
p. 433-438
|
artikel
|
| 68 |
Observer based extremum seeking control for cell population models with uncertain growth dynamics
|
Jerono, P.
|
|
|
59 |
6 |
p. 534-539
|
artikel
|
| 69 |
Offline Reinforcement Learning for Bioprocess Optimization with Historical Data
|
Wang, Haiting
|
|
|
59 |
6 |
p. 67-72
|
artikel
|
| 70 |
On Regularized System Identification from a Martingale Distributional Robustness Perspective
|
Li, Xianyu
|
|
|
59 |
6 |
p. 475-480
|
artikel
|
| 71 |
Optimal control of a microbial growth model by means of substrate concentration and resource allocation
|
Imizcoz, J. Innerarity
|
|
|
59 |
6 |
p. 528-533
|
artikel
|
| 72 |
Optimal Experiment Campaigns under Uncertainty Minimizing Bayes Risk ⁎ ⁎ This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 955520 (Digitalgaesation). CCP and BC gratefully acknowledge funding by Eli Lilly & Company through the Pharmaceutical Systems Engineering Lab (PharmaSEL) and by the Engineering and Physical Sciences Research Council (EPSRC) as part of its Prosperity Partnership Programme under grant EP/T518207/1.
|
Chachuat, Benoît
|
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59 |
6 |
p. 504-509
|
artikel
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| 73 |
Optimizing Parallel Gas Compressor Operations Under Flow Disturbances
|
Dong, Liqiu
|
|
|
59 |
6 |
p. 367-372
|
artikel
|
| 74 |
Parameter Estimation and Model Selection for the Quantitative Analysis of Oncolytic Virus Therapy in Zebrafish ⁎ ⁎ The authors acknowledge funding by the Deutsche Forschungsge-meinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy (EXC 2047—390685813 (Hausdorff Center for Mathematics), EXC 2151—390873048 (ImmunoSensation2)), and under project ID 432325352 – SFB 1454 (Metafammation), by the European Union via ERC grant INTEGRATE, grant agreement number 101126146, and by the University of Bonn (via the Schlegel Professorship of Prof. Dr. Jan Hasenauer)
|
Liu, Yuhong
|
|
|
59 |
6 |
p. 289-294
|
artikel
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| 75 |
Performance Change Recovery in Soft-Sensor Control Loops
|
Zhai, Xuanhui
|
|
|
59 |
6 |
p. 133-138
|
artikel
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| 76 |
Predictive Control of a Chemical Reactor using Multiple Linear Models
|
Vargan, Jozef
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|
|
59 |
6 |
p. 331-336
|
artikel
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| 77 |
Predictive Modelling of Desiccant Drying Processes Using Multi-Feature k-Nearest Neighbours Algorithm
|
Algoufily, Yasser
|
|
|
59 |
6 |
p. 205-210
|
artikel
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| 78 |
Privacy-preserving federated learning for robust approximate MPC
|
Adamek, Joshua
|
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59 |
6 |
p. 1-6
|
artikel
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| 79 |
Ratio and bidirectional control applied to distillation columns
|
Bang, Brage
|
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59 |
6 |
p. 85-90
|
artikel
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| 80 |
Reconstructing Governing Equations of Influenza Virus Dynamics from Incomplete Measurements
|
Steiger, Martin
|
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|
59 |
6 |
p. 540-545
|
artikel
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| 81 |
Robust Economic Model Predictive Control For Continuous Fermentation Processes ⁎ ⁎ This work is supported by MITACS Grant IT-23368 through MITACS-Accelerate Program and Sartorius Canada Inc.
|
Ghodba, Ali
|
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|
59 |
6 |
p. 522-527
|
artikel
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| 82 |
Robust Predictable Control (RPC) for Optimizing Fed-Batch Penicillin Production
|
Simethy, Gary
|
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59 |
6 |
p. 385-390
|
artikel
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| 83 |
Robust Predictive Control for NARX Models from Input-Output Data
|
Azarbahram, Ali
|
|
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59 |
6 |
p. 13-18
|
artikel
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| 84 |
Robust Reaction Rate Estimation with Application to Mammalian Cell Cultures
|
Pimentel, Guilherme A.
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59 |
6 |
p. 451-456
|
artikel
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| 85 |
Smart Optimization of Post-Combustion CO2 Capture from a Coal-fired Power Plant: A Bayesian Framework with Wavelet Neural Networks
|
Jain, Prince Kumar
|
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59 |
6 |
p. 439-444
|
artikel
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| 86 |
Soft Sensor Design using Hierarchical Multi-Fidelity Modeling with Bayesian Optimization for Input Variable Selection
|
Lips, Johannes
|
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59 |
6 |
p. 139-144
|
artikel
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| 87 |
Sparse optimization assisted hybrid data driven modeling of process systems
|
Raj, Abhishek
|
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|
59 |
6 |
p. 313-318
|
artikel
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| 88 |
Sparse Regression Approach to Modelling the Effect of Ionic Liquid Acidity in Biomass Fractionation ⁎ ⁎ Suhaib Nisar is grateful to the Department of Chemical Engineering at Imperial College London for a PhD scholarship and to the EPSRC Centre for Doctoral Training in Next Generation Synthesis & Reaction Technology for the PhD studentship under grant EP/S023232/1.
|
Nisar, Suhaib
|
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59 |
6 |
p. 73-78
|
artikel
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| 89 |
Split parallel control - a little known control structure
|
Forsman, Krister
|
|
|
59 |
6 |
p. 552-557
|
artikel
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| 90 |
Standard MPC and Inputs-Target MPC implementation comparison in ESP systems
|
Costa, Erbet Almeida
|
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59 |
6 |
p. 91-96
|
artikel
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| 91 |
State and Parameter Estimation in Dynamic Real-Time Optimization with Embedded MPC
|
Matias, José
|
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59 |
6 |
p. 181-186
|
artikel
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| 92 |
State estimation for gas purity monitoring and control in water electrolysis systems
|
Cammann, Lucas
|
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59 |
6 |
p. 115-120
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artikel
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| 93 |
Steady state process optimization of an electric flash clay calcination plant
|
Cantisani, Nicola
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59 |
6 |
p. 582-587
|
artikel
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| 94 |
Stochastic data-driven NMPC for partially observable systems using Gaussian processes: a mineral flotation case study
|
Wang, Yicong
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59 |
6 |
p. 109-114
|
artikel
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| 95 |
Surrogate modeling and control optimization of batch crystallization process of β form LGA
|
Song, Bo
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59 |
6 |
p. 343-348
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artikel
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| 96 |
Symmetric Kullback Leibler divergence-based design of experiments with estimation of unspecified values
|
Kumar, Brijesh
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59 |
6 |
p. 510-515
|
artikel
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| 97 |
Systematic Tuning of PI Averaging Level Control for Recycle Systems
|
Gupta, Aayush
|
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|
59 |
6 |
p. 421-426
|
artikel
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| 98 |
Task-optimal data-driven surrogate models for eNMPC via differentiable simulation and optimization
|
Mayfrank, Daniel
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59 |
6 |
p. 43-48
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artikel
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| 99 |
Toward Efficient Global Solutions to Optimal Control Problems via Second-Order Polynomial Approximations ⁎ ⁎ This work was supported by national funds through FCT/MCTES (PIDDAC): LSRE-LCM, UIDB/50020/2020 (DOI: 10.54499/UIDB/50020/2020) and UIDP/50020/2020 (DOI: 10.54499/UIDP/50020/2020); and ALiCE, LA/P/0045/2020 (DOI: 10.54499/LA/P/0045/2020).
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Rodrigues, Diogo
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p. 175-180
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| 100 |
Towards Scalable Bayesian Optimization via Gradient-Informed Bayesian Neural Networks
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Makrygiorgos, Georgios
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59 |
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p. 157-162
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| 101 |
Training Neural ODEs Using Fully Discretized Simultaneous Optimization ⁎ ⁎ Support from a BASF/Royal Academy of Engineering Senior Research Fellowship is gratefully acknowledged.
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Shapovalova, Mariia
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59 |
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p. 469-474
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| 102 |
Trajectory Tracking Control of a Batch Process using Deep Reinforcement Learning
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Unknown Inputs and Reaction Rates Estimation in a CSTR with Full Concentration Vector Measurement
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