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PDEBENCH : an extensive benchmark for scientific machine learning

M. Takamoto, T. Praditia, R. Leiteritz, D. MacKinlay, F. Alesiani, D. Pflüger, and M. Niepert. NIPS '22 : Proceedings of the 36th International Conference on Neural Information Processing Systems, page 1596-1611. Red Hook, Curran Associates Inc, (2022)
DOI: 10.5555/3600270.3600387

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Francesco Virgili University of Stuttgart

Francesco Bruno University of Stuttgart

Francesco Cucinotta University of Stuttgart

Francesco Salerno University of Stuttgart

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Performance of adaptive modulation techniques in the UMTS system.F. Alesiani, E. Biglieri, G. Taricco, and E. Viterbo. GLOBECOM, page 1272-1276. IEEE, (2001)Remote Testimony: How to Trust an Autonomous Vehicle.F. Alesiani, and S. Gajek. VTC Spring, page 1-5. IEEE, (2016)Differential space-time CDMA with turbo decoding.F. Alesiani, and A. Tarable. GLOBECOM, page 616-620. IEEE, (2003)Opportunistic solution-space reduction techniques for reducing the time complexity of Dynamic Speed Control with microsimulation on motorways.K. Gkiotsalitis, and F. Alesiani. ITSC, page 1788-1795. IEEE, (2014)Gated Information Bottleneck for Generalization in Sequential Environments.F. Alesiani, S. Yu, and X. Yu. ICDM, page 1-10. IEEE, (2021)Higher-Rank Irreducible Cartesian Tensors for Equivariant Message PassingV. Zaverkin, F. Alesiani, T. Maruyama, F. Errica, H. Christiansen, M. Takamoto, N. Weber, and M. Niepert. In Proceedings of the 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024), (2024)PDEBench Pretrained Models : Pretrained models for "PDEBench: An Extensive Benchmark for Scientific Machine Learning"M. Takamoto, T. Praditia, R. Leiteritz, D. MacKinlay, F. Alesiani, D. Pflüger, and M. Niepert. Dataset, (2022)Related to: Takamoto, M., Praditia, T., Leiteritz, R., MacKinlay, D., Alesiani, F., Pflüger, D. and Niepert, M.: PDEBench: An Extensive Benchmark for Scientific Machine Learning. submitted to the 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks.Efficient and Scalable Multi-task Regression on Massive Number of Tasks.X. He, F. Alesiani, and A. Shaker. CoRR, (2018)Drift3Flow: Freeway-Incident Prediction Using Real-Time Learning.L. Moreira-Matias, and F. Alesiani. ITSC, page 566-571. IEEE, (2015)PDEBENCH : an extensive benchmark for scientific machine learningM. Takamoto, T. Praditia, R. Leiteritz, D. MacKinlay, F. Alesiani, D. Pflüger, and M. Niepert. NIPS '22 : Proceedings of the 36th International Conference on Neural Information Processing Systems, page 1596-1611. Red Hook, Curran Associates Inc, (2022)