ICCK Transactions on Artificial Intelligence in Space Home About Editors Current Issue
-
CiteScore
-
Impact Factor
  1. Home
  2. Journals
  3. ICCK Transactions on Artificial Intelligence in Space
  4. Volume 1, Issue 1
Solar Flare Forecasting: From Data-driven Towards Physics-informed Machine Learning Models
Review Article | Published: 13 December 2025
ICCK Transactions on Artificial Intelligence in Space Volume 1, Issue 1, 2025: 3-24
Volume 1, Issue 1, ICCK Transactions on Artificial Intelligence in Space
Volume 1, Issue 1, 2025
Submit Manuscript Edit a Special Issue
Article QR Code
Article QR Code
Scan the QR code for reading
Popular articles
Case Studies on Integrating Artificial Intelligence in Finance to Transform Decision Making and Risk Management for Enhanced Financial Outcomes Reinforcement Learning for Prompt Optimization in Language Models: A Comprehensive Survey of Methods, Representations, and Evaluation Challenges AI and the Future of Education: Advancing Personalized Learning and Intelligent Tutoring Systems Plant Disease Detection Using Deep Learning Techniques Modeling Brain Functional Networks Using Graph Neural Networks: A Review and Clinical Application Bridging Modalities: A Survey of Cross-Modal Image-Text Retrieval Research on A Ship Trajectory Classification Method Based on Deep Learning Acrylamide in Food: Sources and Prevention Enhancing Fake News Detection with a Hybrid NLP-Machine Learning Framework Analyzing the Translation and Impact of Popular Science Literature in China: A Case Study Approach
ICCK Transactions on Artificial Intelligence in Space, Volume 1, Issue 1, 2025: 3-24

Free to Read | Review Article | 13 December 2025
Solar Flare Forecasting: From Data-driven Towards Physics-informed Machine Learning Models
by
André Leon S. Gradvohl André Leon S. Gradvohl 1 *
1 Faculdade de Tecnologia, Universidade Estadual de Campinas, Limeira, SP, Brazil
* Corresponding Author: André Leon S. Gradvohl, [email protected]
DOI: 10.62762/TAIS.2025.793969
Received: 08 October 2025, Accepted: 11 November 2025, Published: 13 December 2025  
   PDF  (1.40 MB)
Article Metrics Cite This Article
Abstract
Solar flares are phenomena characterized by the sudden release of accumulated magnetic energy in active regions of the solar magnetosphere. Such liberation occurs through electromagnetic radiation and high-energy particles. Flares appear as intense glows across a broad spectrum, ranging from radio waves to X- or $\gamma$-rays, and last from a few minutes to a few hours. When electromagnetic radiation reaches Earth, it can damage orbiting technologies, disrupting activities that depend on these technologies. This scoping review examines the scientific approaches to solar flare forecasting, covering methods based on physical principles, data-driven approaches using Machine Learning, and their combination in hybrid models. The text highlights the features of each approach. It argues that hybrid models, which use both observational data and knowledge of the physical nature of solar flares, offer a promising strategy. These models, known as Physics-Informed Machine Learning (PIML) models, improve accuracy, robustness, and interpretability. Key PIML strategies integrate prior physical knowledge, such as differential equations or conservation laws, by embedding them into neural network loss functions or utilizing tailored architectures. This integration supports PIML's use by enabling models that are physically plausible and less reliant on large datasets. Notably, reviewed studies show hybrid PIML models improve performance indicators, such as True Skill Statistic and False Alarm Rates, over data-driven methods, reinforcing their value for solar forecasting.
Graphical Abstract
Solar Flare Forecasting: From Data-driven Towards Physics-informed Machine Learning Models
Keywords
solar flare
physics-informed machine learning
data analysis
machine learning
Data Availability Statement
Not applicable.
Funding
This work was supported without any funding.
Conflicts of Interest
The author declares no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
References
  1. Abed, A. K., Qahwaji, R., & Abed, A. (2021). The automated prediction of solar flares from sdo images using deep learning. Advances in Space Research, 67(8), 2544–2557.
    [CrossRef]   [Google Scholar]
  2. Aktukmak, M., Sun, Z., Bobra, M., Gombosi, T., Manchester IV, W. B., Chen, Y., & Hero, A. (2022). Incorporating polar field data for improved solar flare prediction. Frontiers in Astronomy and Space Sciences, 9, 1040107.
    [CrossRef]   [Google Scholar]
  3. Aliferis, C., & Simon, G. (2024). Overfitting, underfitting and general model overconfidence and under-performance pitfalls and best practices in machine learning and AI. Artificial intelligence and machine learning in health care and medical sciences: Best practices and pitfalls, 477-524.
    [CrossRef]   [Google Scholar]
  4. Antiochos, S. K., DeVore, C. R., & Klimchuk, J. A. (1999). A model for solar coronal mass ejections. The Astrophysical Journal, 510(1), 485–493.
    [CrossRef]   [Google Scholar]
  5. Asensio Ramos, A., Cheung, M. C. M., Chifu, I., & Gafeira, R. (2023). Machine learning in solar physics. Living Reviews in Solar Physics, 20(1), 4.
    [CrossRef]   [Google Scholar]
  6. Azari, A. R., Lockhart, J. W., Liemohn, M. W., & Jia, X. (2020). Incorporating physical knowledge into machine learning for planetary space physics. Frontiers in Astronomy and Space Sciences, 7, 36.
    [CrossRef]   [Google Scholar]
  7. Bloomfield, D. S., Higgins, P. A., McAteer, R. T. J., & Gallagher, P. T. (2012). Toward reliable benchmarking of solar flare forecasting methods. The Astrophysical Journal, 747(2), L41.
    [CrossRef]   [Google Scholar]
  8. Bobra, M. G., & Couvidat, S. (2015). Solar flare prediction using SDO/HMI vector magnetic field data with a machine-learning algorithm. The Astrophysical Journal, 798(2), 135.
    [CrossRef]   [Google Scholar]
  9. Bobra, M. G., Sun, X., Hoeksema, J. T., Turmon, M., Liu, Y., Hayashi, K., Barnes, G., & Leka, K. D. (2014). The helioseismic and magnetic imager (hmi) vector magnetic field pipeline: Sharps – space-weather hmi active region patches. Solar Physics, 289(9), 3549–3578.
    [CrossRef]   [Google Scholar]
  10. Camporeale, E. (2019). The challenge of machine learning in space weather: Nowcasting and forecasting. Space Weather, 17(8), 1166–1207.
    [CrossRef]   [Google Scholar]
  11. Carmichael, H. (1964). on the Physics of Solar Flares. In Proc. of AAS-NASA Symp. (Vol. 451). NASA Spec. Pub..
    [Google Scholar]
  12. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357.
    [CrossRef]   [Google Scholar]
  13. Doswell, C. A., Davies-Jones, R., & Keller, D. L. (1990). On summary measures of skill in rare event forecasting based on contingency tables. Weather and Forecasting, 5(4), 576–585.
    [CrossRef]   [Google Scholar]
  14. Farea, A., Yli-Harja, O., & Emmert-Streib, F. (2024). Understanding physics-informed neural networks: Techniques, applications, trends, and challenges. AI, 5(3), 1534–1557.
    [CrossRef]   [Google Scholar]
  15. Florios, K., Kontogiannis, I., Park, S. H., Guerra, J. A., Benvenuto, F., Bloomfield, D. S., & Georgoulis, M. K. (2018). Forecasting solar flares using magnetogram-based predictors and machine learning. Solar Physics, 293(2), 28.
    [CrossRef]   [Google Scholar]
  16. Francisco, G., Berretti, M., Chierichini, S., Mugatwala, R., Fernandes, J., Barata, T., & Del Moro, D. (2025). Limits of solar flare forecasting models and new deep learning approach. The Astrophysical Journal, 985(1), 108.
    [CrossRef]   [Google Scholar]
  17. Goodwin, G. T., Sadykov, V. M., & Martens, P. C. (2024). Investigating performance trends of simulated real-time solar flare predictions: The impacts of training windows, data volumes, and the solar cycle. The Astrophysical Journal, 964(2), 163.
    [CrossRef]   [Google Scholar]
  18. Grim, L. F. L., & Gradvohl, A. L. S. (2024). Solar flare forecasting based on magnetogram sequences learning with multiscale vision transformers and data augmentation techniques. Solar Physics, 299(3), 33.
    [CrossRef]   [Google Scholar]
  19. Guerra, J. A., Murray, S. A., Bloomfield, D. S., & Gallagher, P. T. (2020). Ensemble forecasting of major solar flares: methods for combining models. Journal of Space Weather and Space Climate, 10, 38.
    [CrossRef]   [Google Scholar]
  20. Guastavino, S., Candiani, V., Bemporad, A., Marchetti, F., Benvenuto, F., Massone, A. M., Mancuso, S., Susino, R., Telloni, D., Fineschi, S., & Piana, M. (2023). Physics-driven machine learning for the prediction of coronal mass ejections’ travel times. The Astrophysical Journal, 954(2), 151.
    [CrossRef]   [Google Scholar]
  21. Guastavino, S., Marchetti, F., Benvenuto, F., Campi, C., & Piana, M. (2023). Operational solar flare forecasting via video-based deep learning. Frontiers in Astronomy and Space Sciences, 9, 1039805.
    [CrossRef]   [Google Scholar]
  22. Hanslmeier, A. (2010). The sun and space weather. In Heliophysical Processes (pp. 233-249). Berlin, Heidelberg: Springer Berlin Heidelberg.
    [CrossRef]   [Google Scholar]
  23. Hao, Z., Liu, S., Zhang, Y., Ying, C., Feng, Y., Su, H., & Zhu, J. (2022). Physics-informed machine learning: A survey on problems, methods and applications. arXiv preprint arXiv:2211.08064.
    [Google Scholar]
  24. Hastie, T., Tibshirani, R., Friedman, J., & Franklin, J. (2005). The elements of statistical learning: data mining, inference and prediction. The Mathematical Intelligencer, 27(2), 83-85.
    [Google Scholar]
  25. Heyvaerts, J., Priest, E. R., & Rust, D. M. (1977). An emerging flux model for the solar flare phenomenon. Astrophysical Journal, Part 1, vol. 216, Aug. 15, 1977, p. 123-137., 216, 123-137.
    [CrossRef]   [Google Scholar]
  26. Hirayama, T. (1974). Theoretical model of flares and prominences: I: Evaporating flare model. Solar Physics, 34(2), 323–338.
    [CrossRef]   [Google Scholar]
  27. Hirose, S., Uchida, Y., Uemura, S., Yamaguchi, T., & Cable, S. B. (2001). A Quadruple Magnetic Source Model for Arcade Flares and X-RayArcade Formations outside Active Regions. II. Dark FilamentEruption and the AssociatedArcade Flare. The Astrophysical Journal, 551(1), 586.
    [CrossRef]   [Google Scholar]
  28. Jarolim, R., Thalmann, J. K., Veronig, A. M., & Podladchikova, T. (2023). Probing the solar coronal magnetic field with physics-informed neural networks. Nature Astronomy, 7(10), 1171–1179.
    [CrossRef]   [Google Scholar]
  29. Ji, A., Aydin, B., Georgoulis, M. K., & Angryk, R. (2020). All-clear flare prediction using interval-based time series classifiers. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 4218–4225). IEEE.
    [CrossRef]   [Google Scholar]
  30. Jiao, Z., Sun, H., Wang, X., Manchester, W., Gombosi, T., Hero, A., & Chen, Y. (2020). Solar flare intensity prediction with machine learning models. Space weather, 18(7), e2020SW002440.
    [CrossRef]   [Google Scholar]
  31. Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., & Yang, L. (2021). Physics-informed machine learning. Nature Reviews Physics, 3(6), 422–440.
    [CrossRef]   [Google Scholar]
  32. Kopp, R. A., & Pneuman, G. W. (1976). Magnetic reconnection in the corona and the loop prominence phenomenon. Solar Physics, 50(1), 85-98.
    [CrossRef]   [Google Scholar]
  33. Kusano, K., Iju, T., Bamba, Y., & Inoue, S. (2020). A physics-based method that can predict imminent large solar flares. Science, 369(6503), 587-591.
    [CrossRef]   [Google Scholar]
  34. Lam, H. L., & Samson, J. C. (1994). An investigation of the time-delay between solar events and geomagnetic disturbances using a new method of superposed epoch analysis. Journal of geomagnetism and geoelectricity, 46(2), 107-113.
    [CrossRef]   [Google Scholar]
  35. Leka, K. D., Park, S. H., Kusano, K., Andries, J., Barnes, G., Bingham, S., ... & Terkildsen, M. (2019). A comparison of flare forecasting methods. II. Benchmarks, metrics, and performance results for operational solar flare forecasting systems. The Astrophysical Journal Supplement Series, 243(2), 36.
    [CrossRef]   [Google Scholar]
  36. Li, M., Cui, Y., Luo, B., Ao, X., Liu, S., Wang, J., ... & Wang, X. (2022). Knowledge‐informed deep neural networks for solar flare forecasting. Space weather, 20(8), e2021SW002985.
    [CrossRef]   [Google Scholar]
  37. Li, R., & Zhu, J. (2013). Solar flare forecasting based on sequential sunspot data. Research in Astronomy and Astrophysics, 13(9), 1118–1126.
    [CrossRef]   [Google Scholar]
  38. Li, X., Zheng, Y., Wang, X., & Wang, L. (2020). Predicting solar flares using a novel deep convolutional neural network. The Astrophysical Journal, 891(1), 10.
    [CrossRef]   [Google Scholar]
  39. Liu, C., Deng, N., Wang, J. T. L., & Wang, H. (2017). Predicting solar flares using SDO/HMI vector magnetic data products and the random forest algorithm. The Astrophysical Journal, 843(2), 104.
    [CrossRef]   [Google Scholar]
  40. Liu, H., Liu, C., Wang, J. T. L., & Wang, H. (2019). Predicting solar flares using a long short-term memory network. The Astrophysical Journal, 877(2), 121.
    [CrossRef]   [Google Scholar]
  41. Liu, W., Liu, Y., Zhang, T., Han, Y., Zhou, X., Xie, Y., & Yoo, S. (2022). Use of physics to improve solar forecast: Part ii, machine learning and model interpretability. Solar Energy, 244, 362–378.
    [CrossRef]   [Google Scholar]
  42. Liu, W., Liu, Y., Zhou, X., Xie, Y., Han, Y., Yoo, S., & Sengupta, M. (2021). Use of physics to improve solar forecast: Physics-informed persistence models for simultaneously forecasting GHI, DNI, and DHI. Solar Energy, 215, 252-265.
    [CrossRef]   [Google Scholar]
  43. Lysenko, A. L., Frederiks, D. D., Fleishman, G. D., Aptekar, R. L., Altyntsev, A. T., Golenetskii, S. V., Svinkin, D. S., Ulanov, M., Tsvetkova, A. E., & Ridnaia, A. V. (2020). X-ray and gamma-ray emission from solar flares. Physics-Uspekhi, 63(8), 818–832.
    [CrossRef]   [Google Scholar]
  44. Messerotti, M., Zuccarello, F., Guglielmino, S. L., Bothmer, V., Lilensten, J., Noci, G., ... & Lundstedt, H. (2009). Solar weather event modelling and prediction. Space science reviews, 147(3), 121-185.
    [CrossRef]   [Google Scholar]
  45. Ng, A. Y. (2004, July). Feature selection, L 1 vs. L 2 regularization, and rotational invariance. In Proceedings of the twenty-first international conference on Machine learning (p. 78).
    [CrossRef]   [Google Scholar]
  46. Nishizuka, N., Sugiura, K., Kubo, Y., Den, M., Watari, S. ichi, & Ishii, M. (2017). Solar flare prediction model with three machine-learning algorithms using ultraviolet brightening and vector magnetograms. The Astrophysical Journal, 835(2), 156.
    [CrossRef]   [Google Scholar]
  47. Otto, P., Fassò, A., & Maranzano, P. (2024). A review of regularised estimation methods and cross-validation in spatiotemporal statistics. Statistic Surveys, 18, 299-340.
    [CrossRef]   [Google Scholar]
  48. Pateras, J., Rana, P., & Ghosh, P. (2023). A taxonomic survey of physics-informed machine learning. Applied Sciences, 13(12), 6892.
    [CrossRef]   [Google Scholar]
  49. Popper, K. R. (2010). The logic of scientific discovery (Special Indian edition). Routledge.
    [Google Scholar]
  50. Priest, E. R., Parnell, C. E., & Martin, S. F. (1994). A converging flux model of an x-ray bright point and an associated canceling magnetic feature. The Astrophysical Journal, 427, 459.
    [CrossRef]   [Google Scholar]
  51. Quarteroni, A., Gervasio, P., & Regazzoni, F. (2025). Combining physics-based and data-driven models: advancing the frontiers of research with scientific machine learning. arXiv preprint arXiv:2501.18708.
    [Google Scholar]
  52. Ribeiro, F., & Gradvohl, A. L. S. (2021). Machine learning techniques applied to solar flares forecasting. Astronomy and Computing, 35, 100468.
    [CrossRef]   [Google Scholar]
  53. Seyyedi, A., Bohlouli, M., & Oskoee, S. N. (2024). Machine learning and physics: A survey of integrated models. ACM Computing Surveys, 56(5), 1–33.
    [CrossRef]   [Google Scholar]
  54. Stankewitz, B. (2024). Early stopping for L 2-boosting in high-dimensional linear models. The Annals of Statistics, 52(2), 491-518.
    [CrossRef]   [Google Scholar]
  55. Sturrock, P. A. (1966). Model of the high-energy phase of solar flares. Nature, 211(5050), 695–697.
    [CrossRef]   [Google Scholar]
  56. Sun, H., Manchester IV, W., & Chen, Y. (2021). Improved and interpretable solar flare predictions with spatial and topological features of the polarity inversion line masked magnetograms. Space weather, 19(12), e2021SW002837.
    [CrossRef]   [Google Scholar]
  57. Tang, R., Liao, W., Chen, Z., Zeng, X., Wang, J. S., Luo, B., ... & Wu, Z. (2021). Solar flare prediction based on the fusion of multiple deep-learning models. The Astrophysical Journal Supplement Series, 257(2), 50.
    [CrossRef]   [Google Scholar]
  58. Vural, O., Hamdi, S. M., & Boubrahimi, S. F. (2025). Solar Flare Prediction Using Multivariate Time Series of Photospheric Magnetic Field Parameters: A Comparative Analysis of Vector, Time Series, and Graph Data Representations. Remote Sensing, 17(6), 1075.
    [CrossRef]   [Google Scholar]
  59. Wan, L., Zeiler, M., Zhang, S., Le Cun, Y., & Fergus, R. (2013, May). Regularization of neural networks using dropconnect. In International conference on machine learning (pp. 1058-1066). PMLR.
    [Google Scholar]
  60. Watson, J., Song, C., Weeger, O., Gruner, T., Le, A. T., Pompetzki, K., ... & Hoffman, M. W. (2024). Machine learning with physics knowledge for prediction: A survey. arXiv preprint arXiv:2408.09840.
    [Google Scholar]
  61. Willard, J., Jia, X., Xu, S., Steinbach, M., & Kumar, V. (2022). Integrating scientific knowledge with machine learning for engineering and environmental systems. ACM Computing Surveys, 55(4), 1-37.
    [CrossRef]   [Google Scholar]
Cite This Article
APA Style
Gradvohl, A. L. S. (2025). Solar Flare Forecasting: From Data-driven Towards Physics-informed Machine Learning Models. ICCK Transactions on Artificial Intelligence in Space, 1(1), 3–24. https://doi.org/10.62762/TAIS.2025.793969
Export Citation
RIS Format
Compatible with EndNote, Zotero, Mendeley, and other reference managers
RIS format data for reference managers
TY  - JOUR
AU  - Gradvohl, André Leon S.
PY  - 2025
DA  - 2025/12/13
TI  - Solar Flare Forecasting: From Data-driven Towards Physics-informed Machine Learning Models
JO  - ICCK Transactions on Artificial Intelligence in Space
T2  - ICCK Transactions on Artificial Intelligence in Space
JF  - ICCK Transactions on Artificial Intelligence in Space
VL  - 1
IS  - 1
SP  - 3
EP  - 24
DO  - 10.62762/TAIS.2025.793969
UR  - https://www.icck.org/article/abs/TAIS.2025.793969
KW  - solar flare
KW  - physics-informed machine learning
KW  - data analysis
KW  - machine learning
AB  - Solar flares are phenomena characterized by the sudden release of accumulated magnetic energy in active regions of the solar magnetosphere. Such liberation occurs through electromagnetic radiation and high-energy particles. Flares appear as intense glows across a broad spectrum, ranging from radio waves to X- or $\gamma$-rays, and last from a few minutes to a few hours. When electromagnetic radiation reaches Earth, it can damage orbiting technologies, disrupting activities that depend on these technologies. This scoping review examines the scientific approaches to solar flare forecasting, covering methods based on physical principles, data-driven approaches using Machine Learning, and their combination in hybrid models. The text highlights the features of each approach. It argues that hybrid models, which use both observational data and knowledge of the physical nature of solar flares, offer a promising strategy. These models, known as Physics-Informed Machine Learning (PIML) models, improve accuracy, robustness, and interpretability. Key PIML strategies integrate prior physical knowledge, such as differential equations or conservation laws, by embedding them into neural network loss functions or utilizing tailored architectures. This integration supports PIML's use by enabling models that are physically plausible and less reliant on large datasets. Notably, reviewed studies show hybrid PIML models improve performance indicators, such as True Skill Statistic and False Alarm Rates, over data-driven methods, reinforcing their value for solar forecasting.
SN  - pending
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
BibTeX Format
Compatible with LaTeX, BibTeX, and other reference managers
BibTeX format data for LaTeX and reference managers
@article{Gradvohl2025Solar,
  author = {André Leon S. Gradvohl},
  title = {Solar Flare Forecasting: From Data-driven Towards Physics-informed Machine Learning Models},
  journal = {ICCK Transactions on Artificial Intelligence in Space},
  year = {2025},
  volume = {1},
  number = {1},
  pages = {3-24},
  doi = {10.62762/TAIS.2025.793969},
  url = {https://www.icck.org/article/abs/TAIS.2025.793969},
  abstract = {Solar flares are phenomena characterized by the sudden release of accumulated magnetic energy in active regions of the solar magnetosphere. Such liberation occurs through electromagnetic radiation and high-energy particles. Flares appear as intense glows across a broad spectrum, ranging from radio waves to X- or \$\gamma\$-rays, and last from a few minutes to a few hours. When electromagnetic radiation reaches Earth, it can damage orbiting technologies, disrupting activities that depend on these technologies. This scoping review examines the scientific approaches to solar flare forecasting, covering methods based on physical principles, data-driven approaches using Machine Learning, and their combination in hybrid models. The text highlights the features of each approach. It argues that hybrid models, which use both observational data and knowledge of the physical nature of solar flares, offer a promising strategy. These models, known as Physics-Informed Machine Learning (PIML) models, improve accuracy, robustness, and interpretability. Key PIML strategies integrate prior physical knowledge, such as differential equations or conservation laws, by embedding them into neural network loss functions or utilizing tailored architectures. This integration supports PIML's use by enabling models that are physically plausible and less reliant on large datasets. Notably, reviewed studies show hybrid PIML models improve performance indicators, such as True Skill Statistic and False Alarm Rates, over data-driven methods, reinforcing their value for solar forecasting.},
  keywords = {solar flare, physics-informed machine learning, data analysis, machine learning},
  issn = {pending},
  publisher = {Institute of Central Computation and Knowledge}
}
Article Metrics
Citations:

Google Scholar
0

Crossref

0

Scopus

0

Web of Science

0
Article Access Statistics:
Views: 48
PDF Downloads: 12
Publisher's Note
ICCK stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and Permissions
Institute of Central Computation and Knowledge (ICCK) or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ICCK Transactions on Artificial Intelligence in Space

ICCK Transactions on Artificial Intelligence in Space

ISSN: pending (Online) | ISSN: pending (Print)

Email: [email protected]

Portico

Portico

All published articles are preserved here permanently:
https://www.portico.org/publishers/icck/