| 1 |
Fast forecasting of VGF crystal growth process by dynamic neural networks
Dropka N, Holena M, Ecklebe S, Frank-Rotsch C, Winkler J
Journal of Crystal Growth, 521, 9, 2019 |
| 2 |
Optimization of magnetically driven directional solidification of silicon using artificial neural networks and Gaussian process models
Dropka N, Holena M
Journal of Crystal Growth, 471, 53, 2017 |
| 3 |
Computing the correlation between catalyst composition and its performance in the catalysed process
Holena M, Steinfeldt N, Baerns M, Stefka D
Computers & Chemical Engineering, 43, 55, 2012 |
| 4 |
Generator approach to evolutionary optimization of catalysts and its integration with surrogate modeling
Holena M, Linke D, Rodemerck U
Catalysis Today, 159(1), 84, 2011 |
| 5 |
New catalytic materials for the high-temperature synthesis of hydrocyanic acid from methane and ammonia by high-throughput approach
Moehmel S, Steinfeldt N, Engelschalt S, Holena M, Kolf S, Baerns A, Dingerdissen U, Wolf D, Weber R, Bewersdorf M
Applied Catalysis A: General, 334(1-2), 73, 2008 |
| 6 |
Identification versus generalization comment on the criticism of indeterminacy of artificial neural networks
Holena M
Applied Catalysis A: General, 334(1-2), 381, 2008 |
| 7 |
The influence of preparation variables on the performance of Pd/Al2O3 catalyst in the hydrogenation of 1,3-butadiene: Building a basis for reproducible catalyst synthesis
Cukic T, Kraehnert R, Holena M, Herein D, Linke D, Dingerdissen U
Applied Catalysis A: General, 323, 25, 2007 |
| 8 |
Application of a genetic algorithm and a neural network for the discovery and optimization of new solid catalytic materials
Rodemerck U, Baerns M, Holena M, Wolf D
Applied Surface Science, 223(1-3), 168, 2004 |
| 9 |
Feedforward neural networks in catalysis - A tool for the approximation of the dependency of yield on catalyst composition, and for knowledge extraction
Holena M, Baerns M
Catalysis Today, 81(3), 485, 2003 |
