APA   Click to copy
Popov, A. A., & Sandu, A. (2023). Multifidelity ensemble Kalman filtering using surrogate models defined by theory-guided autoencoders. Data-Driven Modeling and Optimization in Fluid Dynamics: From Physics-Based to Machine Learning Approaches, 16648714, 41.

Chicago/Turabian   Click to copy
Popov, Andrey A, and Adrian Sandu. “Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Theory-Guided Autoencoders.” Data-driven modeling and optimization in fluid dynamics: From physics-based to machine learning approaches 16648714 (2023): 41.

MLA   Click to copy
Popov, Andrey A., and Adrian Sandu. “Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Theory-Guided Autoencoders.” Data-Driven Modeling and Optimization in Fluid Dynamics: From Physics-Based to Machine Learning Approaches, vol. 16648714, Frontiers Media SA, 2023, p. 41.

@article{popov2023a,
  title = {Multifidelity ensemble Kalman filtering using surrogate models defined by theory-guided autoencoders},
  year = {2023},
  journal = {Data-driven modeling and optimization in fluid dynamics: From physics-based to machine learning approaches},
  pages = {41},
  publisher = {Frontiers Media SA},
  volume = {16648714},
  author = {Popov, Andrey A and Sandu, Adrian}
}