SimTech Best Paper Award 2023

SimTech Best Paper Award 2023: Benchmark for ML for scientific simulations

July 25, 2023 / mm

Raphael Leiteritz and Timothy Praditia, two (former) SimTech PhDs, were awarded for their publication "PDEBench: An extensive benchmark for scientific machine learning".
[Picture: SimTech/Max Kovalenko]

The paper is at SimTech’s core topic of data-integrated simulation science, combining simulations with data and AI. The authors developed a benchmark for ML for scientific simulations, providing data and the respective generators together with the simulation codes. The set of more than 10 benchmark problems extends the range of PDEs (partial differential equations) in existing benchmarks, adding more realistic and difficult settings, and offers much more extensive data sets than previously accessible. The authors further published their source codes with user-friendly APIs for data and results generation with popular machine learning models. The paper therefore directly targets data reproducibility and makes use of DaRus, the data repository of the University of Stuttgart, to share the benchmark data and simulation codes.

Takamoto, M., Praditia, T., Leiteritz, R., MacKinlay, D., Alesiani, F., Pflüger, D., & Niepert, M. (2022). PDEBench: An extensive benchmark for scientific machine learning. Advances in Neural Information Processing Systems, 35, 1596-1611. (Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS 2022)).

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Takamoto, Makoto; Praditia, Timothy; Leiteritz, Raphael; MacKinlay, Dan; Alesiani, Francesco; Pflüger, Dirk; Niepert, Mathias, 2022, "PDEBench Datasets",, DaRUS, V3

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SimTech Best Paper Award 2023
Anneli Guthke (left) and Marco Oesting (right) handed over the certificates to Matthias Niepert (second from left) and Dirk Pflüger (second from right) who received it on behalf of Raphael Leiteritz and Timothy Praditia.
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