- Xiao S, Praditia T, Oladyshkin S, Nowak W. Global sensitivity analysis of a thermochemical energy storage model to measure the effect of uncertain parameters. Computational Geosciences. 2020;
- Praditia T, Walser T, Oladyshkin S, Nowak W. Physics-inspired Artificial Neural Network structure improves prediction: Application to a Thermochemical Energy Storage System. Journal of Computational Physics. 2020;
- 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).
- 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]. 2018;22(5):1305–22. Available from: https://doi.org/10.1007/s10596-018-9754-4
- 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. Petroleum Engineering, Institute Teknologi Bandung (Indonesia)
08/2017 M.Sc. Applied Earth Sciences, Technische Universiteit Delft (Netherlands)
Since 01/2018 PhD Student, Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart
Physics-informed Artificial Neural Network