March 09, 2026 article Open access Learning turbulent flows with generative models for super resolution and sparse flow reconstruction Nature Communications DOI: 10.1038/s41467-026-70145-4 Full text (OA) OpenAlex Authors 0 Author list not loaded yet. Cited by 0 stored of 4 References 2 Search Sort Most cited Newest Oldest Patent citations Title Any typearticle review book-chapter conference-paper preprint dissertation book dataset other Any fieldAgricultural and Biological Sciences Arts and Humanities Biochemistry, Genetics and Molecular Biology Business, Management and Accounting Chemical Engineering Chemistry Computer Science Decision Sciences Dentistry Earth and Planetary Sciences Economics, Econometrics and Finance Energy Engineering Environmental Science Health Professions Immunology and Microbiology Materials Science Mathematics Medicine Neuroscience Nursing Pharmacology, Toxicology and Pharmaceutics Physics and Astronomy Psychology Social Sciences Veterinary Open access Using physics-informed enhanced super-resolution generative adversarial networks for subfilter modeling in turbulent reactive flows 2021 Proceedings of the Combustion Institute article Physics and Astronomy Model Reduction and Neural Networks Open access 150 citations Influence of adversarial training on super-resolution turbulence reconstruction 2024 Physical Review Fluids article Physics and Astronomy Model Reduction and Neural Networks Open access 26 citations 2 results