Hayden Schaeffer

1.7k total citations · 1 hit paper
39 papers, 987 citations indexed

About

Hayden Schaeffer is a scholar working on Computational Mechanics, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics. According to data from OpenAlex, Hayden Schaeffer has authored 39 papers receiving a total of 987 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Computational Mechanics, 15 papers in Computer Vision and Pattern Recognition and 11 papers in Statistical and Nonlinear Physics. Recurrent topics in Hayden Schaeffer's work include Sparse and Compressive Sensing Techniques (11 papers), Model Reduction and Neural Networks (11 papers) and Image and Signal Denoising Methods (7 papers). Hayden Schaeffer is often cited by papers focused on Sparse and Compressive Sensing Techniques (11 papers), Model Reduction and Neural Networks (11 papers) and Image and Signal Denoising Methods (7 papers). Hayden Schaeffer collaborates with scholars based in United States, Canada and Hong Kong. Hayden Schaeffer's co-authors include Stanley Osher, Linan Zhang, Scott G. McCalla, Russel E. Caflisch, Cory D. Hauck, Giang Tran, Rachel Ward, Ib A. Svendsen, Thomas Y. Hou and Zecheng Zhang and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Computational Physics and Computer Methods in Applied Mechanics and Engineering.

In The Last Decade

Hayden Schaeffer

34 papers receiving 948 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Learning partial differential equations via data discovery and sparse optimization

2017 275 citations Hayden Schaeffer Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Hayden Schaeffer United States	12	584	259	230	227	212	39	987
Samuel Rudy United States	9	896 1.5×	290 1.1×	333 1.4×	271 1.2×	227 1.1×	17	1.3k
Bethany Lusch United States	11	808 1.4×	480 1.9×	278 1.2×	163 0.7×	187 0.9×	25	1.3k
Bing Yu China	4	655 1.1×	293 1.1×	160 0.7×	119 0.5×	29 0.1×	9	848
Youngsoo Choi United States	16	581 1.0×	317 1.2×	71 0.3×	229 1.0×	50 0.2×	35	809
Felix Dietrich Germany	14	332 0.6×	154 0.6×	135 0.6×	92 0.4×	141 0.7×	47	759
Eurika Kaiser United States	10	529 0.9×	355 1.4×	139 0.6×	128 0.6×	170 0.8×	25	912
Marko Budišić United States	7	598 1.0×	274 1.1×	111 0.5×	166 0.7×	161 0.8×	15	818
Bruce A. Conway United States	28	106 0.2×	181 0.7×	113 0.5×	64 0.3×	262 1.2×	87	3.3k
Jonathan H. Tu United States	9	545 0.9×	718 2.8×	59 0.3×	154 0.7×	105 0.5×	14	1.1k
F. Bernelli Zazzera Italy	27	109 0.2×	83 0.3×	120 0.5×	35 0.2×	412 1.9×	124	1.8k

Countries citing papers authored by Hayden Schaeffer

Since Specialization

Citations

This map shows the geographic impact of Hayden Schaeffer's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hayden Schaeffer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hayden Schaeffer more than expected).

Fields of papers citing papers by Hayden Schaeffer

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hayden Schaeffer. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Hayden Schaeffer. The network helps show where Hayden Schaeffer may publish in the future.

Co-authorship network of co-authors of Hayden Schaeffer

This figure shows the co-authorship network connecting the top 25 collaborators of Hayden Schaeffer. A scholar is included among the top collaborators of Hayden Schaeffer based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Hayden Schaeffer. Hayden Schaeffer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Schaeffer, Hayden, et al.. (2025). TIME-SERIES FORECASTING AND REFINEMENT WITHIN A MULTIMODAL PDE FOUNDATION MODEL. 6(2). 77–89.

Zhang, Zecheng, Christian Moya, Lu Lu, Guang Lin, & Hayden Schaeffer. (2025). DeepONet as a multi-Operator extrapolation model: Distributed pretraining with physics-Informed fine-Tuning. Journal of Computational Physics. 547. 114537–114537.

Liu, Yuxuan, et al.. (2025). Towards a foundation model for partial differential equations: Multioperator learning and extrapolation. Physical review. E. 111(3). 35304–35304. 7 indexed citations

Liu, Yuxuan, et al.. (2025). A Multimodal PDE Foundation Model for Prediction and Scientific Text Descriptions. 1 indexed citations

Zhang, Zecheng, Christian Moya, Lu Lu, Guang Lin, & Hayden Schaeffer. (2024). D2NO: Efficient handling of heterogeneous input function spaces with distributed deep neural operators. Computer Methods in Applied Mechanics and Engineering. 428. 117084–117084. 11 indexed citations

Schaeffer, Hayden, et al.. (2024). PROSE: Predicting Multiple Operators and Symbolic Expressions using multimodal transformers. Neural Networks. 180. 106707–106707. 7 indexed citations

Zhang, Zecheng, Wing Tat Leung, & Hayden Schaeffer. (2024). A discretization-invariant extension and analysis of some deep operator networks. Journal of Computational and Applied Mathematics. 456. 116226–116226. 4 indexed citations

Moya, Christian, et al.. (2024). Bayesian Deep Operator Learning for Homogenized to Fine-Scale Maps for Multiscale PDE. Multiscale Modeling and Simulation. 22(3). 956–972. 8 indexed citations

Chen, Zhijun & Hayden Schaeffer. (2024). Conditioning of random Fourier feature matrices: double descent and generalization error. Information and Inference A Journal of the IMA. 13(2).

10.

Schaeffer, Hayden, et al.. (2023). BelNet: basis enhanced learning, a mesh-free neural operator. Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences. 479(2276). 21 indexed citations

11.

Schaeffer, Hayden, et al.. (2023). HARFE: hard-ridge random feature expansion. 21(2). 7 indexed citations

12.

Liu, Yuxuan, Scott G. McCalla, & Hayden Schaeffer. (2023). Random feature models for learning interacting dynamical systems. Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences. 479(2275). 7 indexed citations

13.

Hashemi, Abolfazl, et al.. (2021). Function Approximation via Sparse Random Features.. arXiv (Cornell University). 2 indexed citations

14.

Ho, Lam Si Tung, Hayden Schaeffer, Giang Tran, & Rachel Ward. (2020). Recovery guarantees for polynomial coefficients from weakly dependent data with outliers. Journal of Approximation Theory. 259. 105472–105472. 8 indexed citations

15.

Schaeffer, Hayden & Scott G. McCalla. (2017). Sparse model selection via integral terms. Physical review. E. 96(2). 23302–23302. 131 indexed citations

16.

Schaeffer, Hayden. (2017). Learning partial differential equations via data discovery and sparse optimization. Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences. 473(2197). 20160446–20160446. 275 indexed citations breakdown →

17.

Tran, Giang, et al.. (2015). An $L^1$ Penalty Method for General Obstacle Problems. SIAM Journal on Applied Mathematics. 75(4). 1424–1444. 20 indexed citations

18.

Ascenzi, Maria‐Grazia, Xia Du, Brian M. de Silva, et al.. (2014). Automated Cell Detection and Morphometry on Growth Plate Images of Mouse Bone. Applied Mathematics. 5(18). 2866–2880. 2 indexed citations

19.

Yang, Yi, Hayden Schaeffer, Wotao Yin, & Stanley Osher. (2013). Mixing space-time derivatives for video compressive sensing. 158–162. 1 indexed citations

20.

Schaeffer, Hayden, et al.. (2013). Boundary detection in echocardiography using a Split Bregman edge detector and a topology preserving level set approach. 66. 73–76. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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