Kontakt
Pfaffenwaldring 5a
D-70569 Stuttgart
Deutschland
Raum: 2.33
2023
- Horuz CC, Karlbauer M, Praditia T, Butz MV, Oladyshkin S, Nowak W, u. a. Physical Domain Reconstruction with Finite Volume Neural Networks. Applied Artificial Intelligence. 2023;37(1):2204261.
2022 (submitted)
- Oladyshkin S, Praditia T, Kröker I, Mohammadi F, Nowak W, Otte S. The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory. Neural Networks.
2022
- Takamoto M, Praditia T, Leiteritz R, MacKinlay D, Alesiani F, Pflüger D, u. a. PDEBench: An Extensive Benchmark for Scientific Machine Learning. In: 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks. 2022. (36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks).
- Horuz CC, Karlbauer M, Praditia T, Butz MV, Oladyshkin S, Nowak W, u. a. Inferring Boundary Conditions in Finite Volume Neural Networks. In: Pimenidis E, Angelov P, Jayne C, Papaleonidas A, Aydin M, Herausgeber. International Conference on Artificial Neural Networks and Machine Learning -- ICANN 2022. Cham: Springer International Publishing; 2022. S. 538–49. (Pimenidis E, Angelov P, Jayne C, Papaleonidas A, Aydin M, Reihenherausgeber. International Conference on Artificial Neural Networks and Machine Learning -- ICANN 2022).
- Karlbauer M, Praditia T, Otte S, Oladyshkin S, Nowak W, Butz MV. Composing Partial Differential Equations with Physics-Aware Neural Networks. In: Proceedings of the 39th International Conference on Machine Learning. Baltimore, USA; 2022. S. 10773--10801. (Proceedings of the 39th International Conference on Machine Learning).
- Praditia T, Karlbauer M, Otte S, Oladyshkin S, Butz MV, Nowak W. Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network. Water Resources Research. 2022;58(12).
2021
- Praditia T, Oladyshkin S, Nowak W. Finite Volume Neural Networks: a Hybrid Modeling Strategy for Subsurface Contaminant Transport. In: AGU Fall Meeting 2021. 2021. (AGU Fall Meeting 2021).
- Praditia T, Karlbauer M, Otte S, Oladyshkin S, Butz M, Nowak W. Finite Volume Neural Network: Modeling Subsurface Contaminant Transport. In: Deep Learning for Simulation ICLR Workshop 2021 [Internet]. 2021. (Deep Learning for Simulation ICLR Workshop 2021). Verfügbar unter: https://arxiv.org/abs/2104.06010
- Praditia T, Oladyshkin S, Nowak W. Physics Informed Neural Network for porous media modelling. In Stuttgart, Germany: InterPore German Chapter Meeting 2021; 2021.
- Praditia T, Oladyshkin S, Nowak W. Universal Differential Equation for Diffusion-Sorption Problem in Porous Media Flow. In online: EGU General Assembly 2021; 2021.
- Xiao S, Praditia T, Oladyshkin S, Nowak W. Global sensitivity analysis of a CaO/Ca(OH)2 thermochemical energy storage model for parametric effect analysis. Applied Energy [Internet]. 2021;285:116456. Verfügbar unter: https://www.sciencedirect.com/science/article/pii/S0306261921000222
- Flaig S, Praditia T, Kissinger A, Lang U, Oladyshkin S, Nowak W. Prognosis of water levels in a moor groundwater system influenced by hydrology and water extraction using an artificial neural network. In online: EGU General Assembly 2021; 2021.
2020
- Praditia T, Walser T, Oladyshkin S, Nowak W. Improving Thermochemical Energy Storage dynamics forecast with Physics-Inspired Neural Network architecture. Energies [Internet]. 2020;13(15):3873. Verfügbar unter: https://www.mdpi.com/1996-1073/13/15/3873
2019
- Praditia T, Walser T, Oladyshkin S, Nowak W. Using physics-based regularization in Artificial Neural Networks to predict thermochemical energy storage systems. In: Fall Meeting 2019, Abstract: IN32B-15. San Francisco, CA, USA: American Geophysical Union (AGU); 2019. (Fall Meeting 2019, Abstract: IN32B-15).
2018
- Praditia T, Helmig R, Hajibeygi H. Multiscale formulation for coupled flow-heat equations arising from single-phase flow in fractured geothermal reservoirs. Computational Geosciences [Internet]. Oktober 2018;22(5):1305–22. Verfügbar unter: https://doi.org/10.1007/s10596-018-9754-4
2017
- Praditia T, Helmig R, Hajibeygi H. Multiscale finite volume method for sequentially coupled flow-heat system of equations in fractured porous media: application to geothermal systems. In Erlangen, Germany: SIAM Conference on Mathematical and Computational Issues in the Geosciences; 2017.
07/2012 B.Sc. Erdöltechnik, Institut Teknologi Bandung (Indonesien)
08/2017 M.Sc. Angewandte Geowissenschaften, Technische Universiteit Delft
(Niederlande)
Seit 01/2018 Doktorand, Institut für Wasser- und
Umweltsystemmodellierung, Lehrstuhl für Stochastische Simulation und Sicherheitsforschung für
Hydrosysteme, Universität Stuttgart
Physik-informiertes künstliches neuronales Netzwerk