Rethinking neural network reparameterized full waveform inversion: A Hessian perspective

Peng Jiang, Sihan Chen, Yuxiao Ren, Senlin Yang, Bin Liu

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DOI: 10.1016/j.neunet.2026.109806
Published: 2027-02
Container: Neural Networks
Publisher: Elsevier BV
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1016/j.neunet.2026.109806,
  title = {Rethinking neural network reparameterized full waveform inversion: A Hessian perspective},
  author = {Peng Jiang and Sihan Chen and Yuxiao Ren and Senlin Yang and Bin Liu},
  year = {2027},
  journal = {Neural Networks},
  doi = {10.1016/j.neunet.2026.109806},
  url = {https://doi.org/10.1016/j.neunet.2026.109806}
}

RIS

TY  - JOUR
TI  - Rethinking neural network reparameterized full waveform inversion: A Hessian perspective
AU  - Peng Jiang
AU  - Sihan Chen
AU  - Yuxiao Ren
AU  - Senlin Yang
AU  - Bin Liu
PY  - 2027
JO  - Neural Networks
DO  - 10.1016/j.neunet.2026.109806
UR  - https://doi.org/10.1016/j.neunet.2026.109806
ER  -

APA

Jiang, P., Chen, S., Ren, Y., Yang, S., & Liu, B. (2027). Rethinking neural network reparameterized full waveform inversion: A Hessian perspective. Neural Networks. https://doi.org/10.1016/j.neunet.2026.109806

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crossref · retrieved 2026-11-09T05:50:24.554Z