Citation notice #9925 · 2026-08-12 12:17:59.929899+00:00
Derivative Computation in PINNs: Automatic Differentiation, Finite Differences and Beyond
cites Pao-Hsiung Chiu, Jian Cheng Wong, Chinchun Ooi, My Ha Dao, and Yew-Soon Ong, which carries a correction notice dated 2025-11-14. One-hop deterministic notice: the citation edge exists in the Pith bibliography graph; no model judged whether the citation was load-bearing.
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01Evidence
Raw extraction · bibliography line · bibliography index 2025
doi: 10.1007/s10915-025-02965-3. Pao-Hsiung Chiu, Jian Cheng Wong, Chinchun Ooi, My Ha Dao, and Yew-Soon Ong. CAN-PINN: A fast physics-informed neural network based on coupled-automatic-numerical differentiation method.Computer Methods in Applied Mechanics and Engineering, 395:114909,
Parser render (TeX stripped for reading; raw above is the evidence)
doi: 10.1007/s10915-025-02965-3. Pao-Hsiung Chiu, Jian Cheng Wong, Chinchun Ooi, My Ha Dao, and Yew-Soon Ong. CAN-PINN: A fast physics-informed neural network based on coupled-automatic-numerical differentiation method.Computer Methods in Applied Mechanics and Engineering, 395:114909
02Event
- Type
- Correction
- Source
- Crossref
- Original DOI
- 10.1007/s10915-025-02965-3
- Notice DOI
- 10.1007/s10915-025-03098-3
- Date
- 2025-11-14
- Title
- Correction to: Automatic Differentiation Is Essential in Training Neural Networks for Solving Differential Equations
- Reasons
- ['Correction']
- Work
- Pao-Hsiung Chiu, Jian Cheng Wong, Chinchun Ooi, My Ha Dao, and Yew-Soon Ong (?)
03Dispute this notice
If this citation does not depend on the flagged claim, or the event is wrong, say so. Disputes are public. For a signed challenge against the paper itself, use the formal challenge form.