| SO-MI: A surrogate model algorithm for computationally expensive nonlinear mixed-integer black-box global optimization problems J Müller, CA Shoemaker, R Piché Computers & operations research 40 (5), 1383-1400, 2013 | 239 | 2013 |
| Influence of Ensemble Surrogate Models and Sampling Strategy on the Solution Quality of Algorithms for Computationally Expensive Black-Box Global Optimization Problems J Müller, CA Shoemaker Journal of Global Optimization 60 (2), 123-144, 2014 | 179 | 2014 |
| Classical optimizers for noisy intermediate-scale quantum devices W Lavrijsen, A Tudor, J Müller, C Iancu, W De Jong 2020 IEEE international conference on quantum computing and engineering (QCE …, 2020 | 140 | 2020 |
| Mixture surrogate models based on Dempster-Shafer theory for global optimization problems J Müller, R Piché Journal of Global Optimization 51, 79-104, 2011 | 124 | 2011 |
| Surrogate optimization of deep neural networks for groundwater predictions J Müller, J Park, R Sahu, C Varadharajan, B Arora, B Faybishenko, ... Journal of Global Optimization 81, 203-231, 2021 | 94 | 2021 |
| MISO: mixed-integer surrogate optimization framework J Müller Optimization and Engineering, 2015 | 90 | 2015 |
| SOCEMO: surrogate optimization of computationally expensive multiobjective problems J Müller INFORMS Journal on Computing 29 (4), 581-596, 2017 | 82 | 2017 |
| SO-I: A Surrogate Model Algorithm for Expensive Nonlinear Integer Programming Problems Including Global Optimization Applications J Müller, CA Shoemaker, R Piche Journal of Global Optimization 59 (4), 865-889, 2014 | 72 | 2014 |
| Approximative solutions to the bicriterion vehicle routing problem with time windows J Müller European Journal of Operational Research 202 (1), 223-231, 2010 | 69 | 2010 |
| MATSuMoTo: The MATLAB surrogate model toolbox for computationally expensive black-box global optimization problems J Mueller arXiv preprint arXiv:1404.4261, 2014 | 66 | 2014 |
| Long-term missing value imputation for time series data using deep neural networks J Park, J Müller, B Arora, B Faybishenko, G Pastorello, C Varadharajan, ... Neural Computing and Applications 35 (12), 9071-9091, 2023 | 50 | 2023 |
| Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C Varadharajan, AP Appling, B Arora, DS Christianson, VC Hendrix, ... Hydrological Processes 36 (4), e14565, 2022 | 48 | 2022 |
| Surrogate optimization of computationally expensive black-box problems with hidden constraints J Müller, M Day INFORMS Journal on Computing 31 (4), 689-702, 2019 | 45 | 2019 |
| CH4 Parameter Estimation in CLM4.5bgc Using Surrogate Global Optimization J Muller, R Paudel, CA Shoemaker, J Woodbury, Y Wang, N Mahowald Geoscientific Model Development 8 (10), 3285-3310, 2015 | 43 | 2015 |
| GOSAC: global optimization with surrogate approximation of constraints J Müller, JD Woodbury Journal of Global Optimization 69 (1), 117-136, 2017 | 41 | 2017 |
| Impact of input feature selection on groundwater level prediction from a multi-layer perceptron neural network RK Sahu, J Müller, J Park, C Varadharajan, B Arora, B Faybishenko, ... Frontiers in Water 2, 573034, 2020 | 39 | 2020 |
| Pushing the frontiers in climate modelling and analysis with machine learning V Eyring, WD Collins, P Gentine, EA Barnes, M Barreiro, T Beucler, ... Nature Climate Change 14 (9), 916-928, 2024 | 30 | 2024 |
| User guide for modularized surrogate model toolbox J Müller Tampere University of Technology, 2012 | 28 | 2012 |
| Apprentice for event generator tuning M Krishnamoorthy, H Schulz, X Ju, W Wang, S Leyffer, Z Marshall, ... EPJ Web of Conferences 251, 03060, 2021 | 24 | 2021 |
| 2020 IEEE International Conference on Quantum Computing and Engineering (QCE) W Lavrijsen, A Tudor, J Müller, C Iancu, W De Jong IEEE, 2020 | 24 | 2020 |
Robert PicheTampere University of TechnologyE-mailová adresa ověřena na: tuni.fi