|We're delighted to announce that our recently published paper, "PDEBENCH: An Extensive Benchmark for Scientific Machine Learning," has been awarded the SimTech Best Paper Award 2023 at this year's SimTech Status Seminar in Bad Boll. The paper has already garnered over 30 citations since its January publication and the accompanying dataset has been accessed over 13,000 times by researchers worldwide.
In collaboration with various institutions, both within and beyond the University of Stuttgart, we aimed to advance scientifically motivated benchmarks for machine learning. Our extensive suite comprises 11 diverse scenarios of 1-, 2-, and 3-D datasets generated from various PDE problems. We also provide implementations of state-of-the-art baseline models with pre-trained weights for easy comparisons.
We would like to thank all collaborators for their support in this endeavor. This recognition and the substantial response to our paper and dataset reaffirm its importance in the domain of Scientific Machine Learning, paving the way for future developments.