Recoverable Identifier
advisory
doi_compliance
recoverable_identifier
DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1103/PhysRevE.111.035304.URL) was visible in the surrounding text but could not be confirmed against doi.org as printed.
Paper page Integrity report arXiv Try DOI
Evidence text
URL https://openreview.net/forum? id=0r9mhjRv1E. Siddik, A. B., Oyen, D., Most, A., Kucer, M., and Biswas, A. SPUS: A Lightweight and Parameter-Efficient Foun- dation Model for PDEs, 2025. URL https://arxiv. org/abs/2510.01370. Song, Z., Yuan, J., and Yang, H. FMint: Bridging Human Designed and Data Pretrained Models for Differential Equation Foundation Model, 2024. URL https:// arxiv.org/abs/2404.14688. Subramanian, S., Harrington, P., Keutzer, K., Bhimji, W., Morozov, D., Mahoney, M. W., and Gholami, A. Towards foundation models for scientific machine learning: char- acterizing scaling and transfer behavior. In Advances in Neural Information Processing Systems, NeurIPS, Red Hook, NY , USA, 2023. Curran Associates Inc. URL https://arxiv.org/abs/2306.00258. Sun, J., Liu, Y ., Zhang, Z., and Schaeffer, H. Towards a foundation model for partial differential equations: Multioperator learning and extrapolation. Phys. Rev. E, 111:035304, Mar 2025. doi: 10.1103/PhysRevE.111. 035304. URL https://link.aps.org/doi/10. 1103/PhysRevE.111.035304. Terraz, T., Ribes, A., Fournier, Y ., Iooss, B., and Raf- fin, B. Melissa: large scale in transit sensitivity anal- ysis avoiding intermediate files. In Proceedings of the International Conference for High Performance Com- puting, Networking, Storage and Analysis , SC ’17, New York, NY , USA, 2017. Association for Comput- ing Machinery. ISBN 9781450351140. doi: 10.1145/ 3126908.3126922. URL https://doi.org/10. 1145/3126908.3126922. Thuerey, N.,
Evidence payload
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"reconstructed_doi": "10.1103/PhysRevE.111.035304.URL",
"ref_index": 7,
"resolved_title": null,
"verdict_class": "incontrovertible"
}