| 1 |
Optimization of DEM parameters using multi-objective reinforcement learning
Westbrink F, Elbel A, Schwung A, Ding SX
Powder Technology, 379, 602, 2021 |
| 2 |
DEM modelling for flow of cohesive lignocellulosic biomass powders: Model calibration using bulk tests
Pachon-Morales J, Do H, Colin J, Puel F, Perre P, Schott D
Advanced Powder Technology, 30(4), 732, 2019 |
| 3 |
A calibration framework for discrete element model parameters using genetic algorithms
Do HQ, Aragon AM, Schott DL
Advanced Powder Technology, 29(6), 1393, 2018 |
| 4 |
Proton Exchange Membrane Water Electrolysis Modeling for System Simulation and Degradation Analysis
Gossling S, Stypka S, Bahr M, Oberschachtsiek B, Heinzel A
Chemie Ingenieur Technik, 90(10), 1437, 2018 |
| 5 |
Gaussian Mixture Model-Based Ensemble Kalman Filter for Machine Parameter Calibration
Fan R, Huang RK, Diao RS
IEEE Transactions on Energy Conversion, 33(3), 1597, 2018 |
| 6 |
First-harmonic nonlinearities can predict unseen third-harmonics in medium-amplitude oscillatory shear (MAOS)
Torre OCDL, Ewoldt RH
Korea-Australia Rheology Journal, 30(1), 1, 2018 |
| 7 |
Calibration of DEM models for irregular particles based on experimental Check for design method and bulk experiments
Zhou HL, Hu ZQ, Chen JG, Lv X, Xie N
Powder Technology, 332, 210, 2018 |
| 8 |
Prediction of La0.6Sr0.4CO0.2Fe0.8O3 cathode microstructures during sintering: Kinetic Monte Carlo (KMC) simulations calibrated by artificial neural networks
Yan ZL, Kim Y, Hara S, Shikazono N
Journal of Power Sources, 346, 103, 2017 |
| 9 |
Effect of pH, substrate and free nitrous acid concentrations on ammonium oxidation rate
Jimenez E, Gimenez JB, Seco A, Ferrer J, Serralta J
Bioresource Technology, 124, 478, 2012 |
| 10 |
Effect of pH and nitrite concentration on nitrite oxidation rate
Jimenez E, Gimenez JB, Ruano MV, Ferrer J, Serralta J
Bioresource Technology, 102(19), 8741, 2011 |
