Dieses Bild zeigt Timothy Praditia

Timothy Praditia

Herr M.Sc.

Doktorand
Institut für Wasser- und Umweltsystemmodellierung (LS3/SimTech)

Kontakt

Pfaffenwaldring 5a
D-70569 Stuttgart
Deutschland
Raum: 2.33

  1. 2022 (submitted)

    1. 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. (36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks).
    2. 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.
    3. 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.
  2. 2022

    1. 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).
    2. Horuz CC, Karlbauer M, Praditia T, Butz MV, Oladyshkin S, Nowak W, u. a. Inferring Boundary Conditions in Finite Volume Neural Networks. In: International Conference on Artificial Neural Networks 2022. 2022. (International Conference on Artificial Neural Networks 2022).
  3. 2021

    1. 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
    2. 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).
    3. 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.
    4. Praditia T, Oladyshkin S, Nowak W. Physics Informed Neural Network for porous media modelling. In Stuttgart, Germany: InterPore German Chapter Meeting 2021; 2021.
    5. 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
    6. Praditia T, Oladyshkin S, Nowak W. Universal Differential Equation for Diffusion-Sorption Problem in Porous Media Flow. In online: EGU General Assembly 2021; 2021.
  4. 2020

    1. 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
  5. 2019

    1. 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).
  6. 2018

    1. 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
  7. 2017

    1. 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

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