| High‐dimensional posterior exploration of hydrologic models using multiple‐try DREAM(ZS) and high‐performance computing E Laloy, JA Vrugt Water Resources Research 48 (1), 2012 | 557 | 2012 |
| Training‐image based geostatistical inversion using a spatial generative adversarial neural network E Laloy, R Hérault, D Jacques, N Linde Water Resources Research 54 (1), 381-406, 2018 | 377 | 2018 |
| Efficient posterior exploration of a high‐dimensional groundwater model from two‐stage Markov chain Monte Carlo simulation and polynomial chaos expansion E Laloy, B Rogiers, JA Vrugt, D Mallants, D Jacques Water Resources Research 49 (5), 2664-2682, 2013 | 279 | 2013 |
| Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network E Laloy, R Hérault, J Lee, D Jacques, N Linde Advances in water resources 110, 387-405, 2017 | 229 | 2017 |
| HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community C Shen, E Laloy, A Elshorbagy, A Albert, J Bales, FJ Chang, S Ganguly, ... Hydrology and Earth System Sciences 22 (11), 5639-5656, 2018 | 224 | 2018 |
| Effect of intercropping period management on runoff and erosion in a maize cropping system E Laloy, CL Bielders Journal of environmental quality 39 (3), 1001-1008, 2010 | 78 | 2010 |
| Broadening the use of machine learning in hydrology C Shen, X Chen, E Laloy Frontiers in Water 3, 681023, 2021 | 68 | 2021 |
| Deep generative models in inversion: The impact of the generator's nonlinearity and development of a new approach based on a variational autoencoder J Lopez-Alvis, E Laloy, F Nguyen, T Hermans Computers & Geosciences 152, 104762, 2021 | 67 | 2021 |
| Parameter optimization and uncertainty analysis for plot-scale continuous modeling of runoff using a formal Bayesian approach E Laloy, D Fasbender, CL Bielders Journal of hydrology 380 (1-2), 82-93, 2010 | 64 | 2010 |
| Gradient-based deterministic inversion of geophysical data with generative adversarial networks: is it feasible? E Laloy, N Linde, C Ruffino, R Hérault, G Gasso, D Jacques Computers & Geosciences 133, 104333, 2019 | 61 | 2019 |
| Mass conservative three‐dimensional water tracer distribution from Markov chain Monte Carlo inversion of time‐lapse ground‐penetrating radar data E Laloy, N Linde, JA Vrugt Water Resources Research 48 (7), 2012 | 60 | 2012 |
| Electrical resistivity in a loamy soil: Identification of the appropriate pedo‐electrical model E Laloy, M Javaux, M Vanclooster, C Roisin, CL Bielders Vadose Zone Journal 10 (3), 1023-1033, 2011 | 59 | 2011 |
| Probabilistic inference of multi‐G aussian fields from indirect hydrological data using circulant embedding and dimensionality reduction E Laloy, N Linde, D Jacques, JA Vrugt Water Resources Research 51 (6), 4224-4243, 2015 | 57 | 2015 |
| Merging parallel tempering with sequential geostatistical resampling for improved posterior exploration of high-dimensional subsurface categorical fields E Laloy, N Linde, D Jacques, G Mariethoz Advances in water resources 90, 57-69, 2016 | 40 | 2016 |
| Effect of high-resolution spatial soil moisture variability on simulated runoff response using a distributed hydrologic model J Minet, E Laloy, S Lambot, M Vanclooster Hydrology and Earth System Sciences 15 (4), 1323-1338, 2011 | 39 | 2011 |
| How efficient are one-dimensional models to reproduce the hydrodynamic behavior of structured soils subjected to multi-step outflow experiments? E Laloy, M Weynants, CL Bielders, M Vanclooster, M Javaux Journal of hydrology 393 (1-2), 37-52, 2010 | 39 | 2010 |
| Plot scale continuous modelling of runoff in a maize cropping system with dynamic soil surface properties E Laloy, CL Bielders Journal of Hydrology 349 (3-4), 455-469, 2008 | 36 | 2008 |
| Nested multiresolution hierarchical simulated annealing algorithm for porous media reconstruction L Lemmens, B Rogiers, D Jacques, M Huysmans, R Swennen, JL Urai, ... Physical Review E 100 (5), 053316, 2019 | 32 | 2019 |
| Emulation of CPU-demanding reactive transport models: a comparison of Gaussian processes, polynomial chaos expansion, and deep neural networks E Laloy, D Jacques Computational Geosciences 23, 1193-1215, 2019 | 32 | 2019 |
| Bayesian full-waveform tomography with application to crosshole ground penetrating radar data J Hunziker, E Laloy, N Linde Geophysical Journal International 218 (2), 913-931, 2019 | 31 | 2019 |
