Replace preprint entry with published article for Jakhar et al. on AI-discovered turbulence closure

Author: amarchakitus

_bibliography/papers.bib

@@ -15,22 +15,6 @@ @misc{zhou_reframing_2025
 	preprint = "https://arxiv.org/abs/2509.26282",
 }
 
-@misc{jakhar_analytical_2025,
-	title = {An {Analytical} and {AI}-discovered {Stable}, {Accurate}, and {Generalizable} {Subgrid}-scale {Closure} for {Geophysical} {Turbulence}},
-	url = {http://arxiv.org/abs/2509.20365},
-	doi = {10.48550/arXiv.2509.20365},
-	abstract = {By combining AI and fluid physics, we discover a closed-form closure for 2D turbulence from small direct numerical simulation (DNS) data. Large-eddy simulation (LES) with this closure is accurate and stable, reproducing DNS statistics including those of extremes. We also show that the new closure could be derived from a 4th-order truncated Taylor expansion. Prior analytical and AI-based work only found the 2nd-order expansion, which led to unstable LES. The additional terms emerge only when inter-scale energy transfer is considered alongside standard reconstruction criterion in the sparse-equation discovery.},
-	urldate = {2025-10-07},
-	publisher = {arXiv},
-	author = {Jakhar, Karan and Guan, Yifei and Hassanzadeh, Pedram},
-	month = oct,
-	year = {2025},
-	note = {arXiv:2509.20365 [physics]},
-	keywords = {Computer Science - Machine Learning, Physics - Atmospheric and Oceanic Physics},
-	file = {Preprint PDF:/Users/marchakitus/Zotero/storage/YVGZ9DJF/Jakhar et al. - 2025 - An Analytical and AI-discovered Stable, Accurate, and Generalizable Subgrid-scale Closure for Geophy.pdf:application/pdf;Snapshot:/Users/marchakitus/Zotero/storage/73ANKXS2/2509.html:text/html},
-	preprint = "https://arxiv.org/abs/2509.20365",
-}
-
 @misc{jiang_hierarchical_2025,
 	title = {Hierarchical {Implicit} {Neural} {Emulators}},
 	url = {http://arxiv.org/abs/2506.04528},
@@ -1926,4 +1910,22 @@ @misc{masiwal_decision-oriented_2026
 	keywords = {Computer Science - Machine Learning, Physics - Atmospheric and Oceanic Physics, Computer Science - Artificial Intelligence, Economics - General Economics},
 	file = {Preprint PDF:/Users/marchakitus/Zotero/storage/ILQARKHS/Masiwal et al. - 2026 - Decision-oriented benchmarking to transform AI weather forecast access Application to the Indian mo.pdf:application/pdf;Snapshot:/Users/marchakitus/Zotero/storage/GIV9YMN3/2602.html:text/html},
 	preprint = "http://arxiv.org/abs/2602.03767",
-}
+}
+
+@article{jakhar_analytical_2026,
+	title = {Analytical and {AI}-{Discovered} {Stable}, {Accurate}, and {Generalizable} {Subgrid}-{Scale} {Closure} for {Geophysical} {Turbulence}},
+	volume = {136},
+	issn = {0031-9007, 1079-7114},
+	url = {https://link.aps.org/doi/10.1103/v28b-5qmp},
+	doi = {10.1103/v28b-5qmp},
+	abstract = {By combining artificial intelligence and fluid physics, we discover a closed-form closure for 2D turbulence from small direct numerical simulation data. Large-eddy simulation with this closure is accurate and stable, reproducing direct numerical simulation statistics, including those of extremes. We also show that the new closure could be derived from a fourth-order truncated Taylor expansion. Prior analytical and artificial-intelligence-based work only found the second-order expansion, which led to unstable large-eddy simulation. The additional terms emerge only when interscale energy transfer is considered alongside standard reconstruction criterion in the sparse-equation discovery.},
+	language = {en},
+	number = {6},
+	urldate = {2026-02-10},
+	journal = {Physical Review Letters},
+	author = {Jakhar, Karan and Guan, Yifei and Hassanzadeh, Pedram},
+	month = feb,
+	year = {2026},
+	pages = {064201},
+	preprint = "http://arxiv.org/abs/2509.20365",
+}