| Pdebench: An extensive benchmark for scientific machine learning M Takamoto, T Praditia, R Leiteritz, D MacKinlay, F Alesiani, D Pflüger, ... Advances in Neural Information Processing Systems 35, 1596-1611, 2022 | 121 | 2022 |
| Multiscale formulation for coupled flow-heat equations arising from single-phase flow in fractured geothermal reservoirs T Praditia, R Helmig, H Hajibeygi Computational Geosciences 22 (5), 1305–1322, 2018 | 46 | 2018 |
| Finite volume neural network: Modeling subsurface contaminant transport T Praditia, M Karlbauer, S Otte, S Oladyshkin, MV Butz, W Nowak arXiv preprint arXiv:2104.06010, 2021 | 17 | 2021 |
| Composing partial differential equations with physics-aware neural networks M Karlbauer, T Praditia, S Otte, S Oladyshkin, W Nowak, MV Butz International Conference on Machine Learning, 10773-10801, 2022 | 16 | 2022 |
| Global sensitivity analysis of a CaO/Ca (OH) 2 thermochemical energy storage model for parametric effect analysis S Xiao, T Praditia, S Oladyshkin, W Nowak Applied Energy 285, 116456, 2021 | 9 | 2021 |
| Improving thermochemical energy storage dynamics forecast with physics-inspired neural network architecture T Praditia, T Walser, S Oladyshkin, W Nowak Energies 13 (15), 3873, 2020 | 8 | 2020 |
| Learning groundwater contaminant diffusion‐sorption processes with a finite volume neural network T Praditia, M Karlbauer, S Otte, S Oladyshkin, MV Butz, W Nowak Water Resources Research 58 (12), e2022WR033149, 2022 | 7 | 2022 |
| Physics-informed neural networks for learning dynamic, distributed and uncertain systems T Praditia Stuttgart: Eigenverlag des Instituts für Wasser-und Umweltsystemmodellierung, 2023 | 5 | 2023 |
| Infering boundary conditions in finite volume neural networks CC Horuz, M Karlbauer, T Praditia, MV Butz, S Oladyshkin, W Nowak, ... International Conference on Artificial Neural Networks, 538-549, 2022 | 3 | 2022 |
| The deep arbitrary polynomial chaos neural network or how Deep Artificial Neural Networks could benefit from data-driven homogeneous chaos theory S Oladyshkin, T Praditia, I Kroeker, F Mohammadi, W Nowak, S Otte Neural Networks 166, 85-104, 2023 | 2 | 2023 |
| Physical domain reconstruction with finite volume neural networks CC Horuz, M Karlbauer, T Praditia, MV Butz, S Oladyshkin, W Nowak, ... Applied Artificial Intelligence 37 (1), 2204261, 2023 | 1 | 2023 |
| Finite Volume Neural Networks: a Hybrid Modeling Strategy for Subsurface Contaminant Transport T Praditia, S Oladyshkin, W Nowak AGU Fall Meeting Abstracts 2021, H34F-02, 2021 | | 2021 |
| Finite volume neural network: Modeling subsurface contaminant transport M Karlbauer, S Otte, MV Butz, T Praditia, S Oladyshkin, W Nowak ArXiv 2104, 2021 | | 2021 |
| Universal Differential Equation for Diffusion-Sorption Problem in Porous Media Flow T Praditia, S Oladyshkin, W Nowak EGU General Assembly Conference Abstracts, EGU21-49, 2021 | | 2021 |
| Prognosis of water levels in a moor groundwater system influenced by hydrology and water extraction using an artificial neural network S Flaig, T Praditia, A Kissinger, U Lang, S Oladyshkin, W Nowak EGU General Assembly Conference Abstracts, EGU21-3013, 2021 | | 2021 |
| Using physics-based regularization in Artificial Neural Networks to predict thermochemical energy storage systems T Praditia, T Walser, S Oladyshkin, W Nowak AGU Fall Meeting Abstracts 2019, IN32B-15, 2019 | | 2019 |
| Multiscale Finite Volume Method for Coupled Single-Phase Flow and Heat Equations in Fractured Porous Media: Application to Geothermal Systems T Praditia TU Delft, 2017 | | 2017 |
| Prognosis of water levels in a moor groundwater system influenced by hydrology and water extraction using an artificial neural network (EGU21-3013) L outperforms MODFLOW, S Flaig, T Praditia, A Kissinger, U Lang, ... | | |
| Universal Differential Equation for Diffusion-Sorption Problem in Porous Media Flow (EGU21-49) T Praditia, S Oladyshkin, W Nowak | | |
Sergey OladyshkinProfessor, University of Stuttgart在 iws.uni-stuttgart.de 的电子邮件经过验证
