| Combining machine learning and domain decomposition methods for the solution of partial differential equations—a review A Heinlein, A Klawonn, M Lanser, J Weber GAMM‐Mitteilungen 44 (1), e202100001, 2021 | 71 | 2021 |
| Machine learning in adaptive domain decomposition methods---predicting the geometric location of constraints A Heinlein, A Klawonn, M Lanser, J Weber SIAM Journal on Scientific Computing 41 (6), A3887-A3912, 2019 | 42 | 2019 |
| Estimating the time-dependent contact rate of SIR and SEIR models in mathematical epidemiology using physics-informed neural networks V Grimm, A Heinlein, A Klawonn, M Lanser, J Weber Electron. Trans. Numer. Anal 56, 1-27, 2022 | 34 | 2022 |
| Combining Machine Learning and Adaptive Coarse Spaces---A Hybrid Approach for Robust FETI-DP Methods in Three Dimensions A Heinlein, A Klawonn, M Lanser, J Weber SIAM Journal on Scientific Computing 43 (5), S816-S838, 2021 | 18 | 2021 |
| A frugal FETI-DP and BDDC coarse space for heterogeneous problems A Heinlein, A Klawonn, M Lanser, J Weber Universität zu Köln, 2019 | 17 | 2019 |
| Preconditioning the coarse problem of BDDC methods-three-level, algebraic multigrid, and vertex-based preconditioners A Klawonn, M Lanser, O Rheinbach, J Weber Universität zu Köln, 2019 | 11 | 2019 |
| A Domain Decomposition–Based CNN-DNN Architecture for Model Parallel Training Applied to Image Recognition Problems A Klawonn, M Lanser, J Weber SIAM Journal on Scientific Computing 46 (5), C557-C582, 2024 | 10 | 2024 |
| Machine learning and domain decomposition methods-a survey A Klawonn, M Lanser, J Weber Computational Science and Engineering 1 (1), 2, 2024 | 9 | 2024 |
| Predicting the geometric location of critical edges in adaptive GDSW overlapping domain decomposition methods using deep learning A Heinlein, A Klawonn, M Lanser, J Weber Domain Decomposition Methods in Science and Engineering XXVI, 307-315, 2023 | 7 | 2023 |
| Estimating the time-dependent contact rate of SIR and SEIR models in mathematical epidemiology using physics-informed neural networks V Grimm, A Heinlein, A Klawonn, M Lanser, J Weber Universität zu Köln, 2020 | 7 | 2020 |
| Machine learning in adaptive FETI-DP: Reducing the effort in sampling A Heinlein, A Klawonn, M Lanser, J Weber Numerical Mathematics and Advanced Applications ENUMATH 2019: European …, 2020 | 7 | 2020 |
| Machine learning in adaptive FETI-DP–a comparison of smart and random training data A Heinlein, A Klawonn, M Lanser, J Weber Domain Decomposition Methods in Science and Engineering XXV 25, 218-226, 2020 | 6 | 2020 |
| Learning adaptive coarse basis functions of FETI-DP A Klawonn, M Lanser, J Weber Journal of Computational Physics 496, 112587, 2024 | 5 | 2024 |
| Machine Learning in Adaptive FETI-DP-A Comparison of Smart and Random Training Data,(2018) A Heinlein, A Klawonn, M Lanser, J Weber TR series, Center for Data and Simulation Science, University of Cologne …, 0 | 5 | |
| Efficient and robust FETI-DP and BDDC methods--Approximate coarse spaces and deep learning-based adaptive coarse spaces J Weber Universität zu Köln, 2022 | 4 | 2022 |
| Learning adaptive FETI-DP constraints for irregular domain decompositions A Klawonn, M Lanser, J Weber Domain Decomposition Methods in Science and Engineering XXVII, 279-286, 2024 | 3 | 2024 |
| Combining Machine Learning and Domain Decomposition Methods–A Review A Heinlein, A Klawonn, M Lanser, J Weber Universität zu Köln, 2020 | 2 | 2020 |
| Model Parallel Training and Transfer Learning for Convolutional Neural Networks by Domain Decomposition A Klawonn, M Lanser, J Weber arXiv preprint arXiv:2408.14442, 2024 | 1 | 2024 |
| Domain-decomposed image classification algorithms using linear discriminant analysis and convolutional neural networks A Klawonn, M Lanser, J Weber arXiv preprint arXiv:2410.23359, 2024 | | 2024 |
| Adaptive Three-Level BDDC Using Frugal Constraints A Klawonn, M Lanser, J Weber Domain Decomposition Methods in Science and Engineering XXVII, 287-294, 2024 | | 2024 |
Martin LanserAkademischer Rat, Mathematisches Institut, Universität zu KölnПотвърден имейл адрес: uni-koeln.de
