When AI meets physics: Beyond prediction, towards understanding
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Keywords:
- Artificial intelligence /
- physics /
- interpretability /
- physical priors /
- scientific discovery
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REFERENCES
[1] Xu Y., Liu X., Cao X., et al. (2021). Artificial intelligence: A powerful paradigm for scientific research. The Innovation 2:100179. DOI:10.1016/j.xinn.2021.100179 [2] Batzner S., Musaelian A., Sun L., et al. (2022). E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat. Commun. 13:2453. DOI:10.1038/s41467-022-29939-5 [3] Cory-Wright R., Cornelio C., Dash S., et al. (2024). Evolving scientific discovery by unifying data and background knowledge with AI Hilbert. Nat. Commun. 15:5922. DOI:10.1038/s41467-024-50074-w [4] Zeni C., Pinsler R., Zügner D., et al. (2025). A generative model for inorganic materials design. Nature 639:624-632. DOI:10.1038/s41586-025-08628-5 [5] Gao D., Lu S., Zhang C., et al. (2026). Autonomous closed-loop framework for reproducible perovskite solar cells.Nature 653:707-714. DOI:10.1038/s41586-026-10482-y -
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Cite this article:
Li J., Ren R.C ., Zhao Z.X., et al. (2026). When AI meets physics: Beyond prediction, towards understanding. The Innovation Physics 1:100006. https://doi.org/10.59717/j.tip.2026.100006
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Li J., Ren R.C ., Zhao Z.X., et al. (2026). When AI meets physics: Beyond prediction, towards understanding. The Innovation Physics 1:100006. https://doi.org/10.59717/j.tip.2026.100006
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Figure 1.
From prediction to understanding in AI-driven physics Black-box models achieve high accuracy but lack insight. Embedding physical priors enables interpretable AI that reveals laws, respects constraints, and supports extrapolation.
