2 code implementations • 10 Apr 2024 • Chuanqi Chen, Nan Chen, Jin-Long Wu
Then, neural networks are supplemented to the knowledge-based model in a specific way, which not only characterizes the remaining features that are challenging to model with simple forms but also advances the use of analytic formulae to efficiently compute the nonlinear DA solution.
1 code implementation • 29 Dec 2023 • Jin-Long Wu, Matthew E. Levine, Tapio Schneider, Andrew Stuart
Complex dynamical systems are notoriously difficult to model because some degrees of freedom (e. g., small scales) may be computationally unresolvable or are incompletely understood, yet they are dynamically important.
1 code implementation • 20 Nov 2023 • Chuanqi Chen, Jin-Long Wu
In this work, we build on the recent progress of operator learning and present a data-driven modeling framework that is continuous in both space and time.
1 code implementation • 16 Apr 2023 • Chuanqi Chen, Nan Chen, Jin-Long Wu
In addition, the CEBoosting method is applied to a nonlinear paradigm model for topographic mean flow interaction, demonstrating the online detection of regime switching in the presence of strong intermittency and extreme events.
1 code implementation • 15 Nov 2019 • Zeng Yang, Jin-Long Wu, Heng Xiao
Recently, GANs have been used to emulate complex physical systems such as turbulent flows.
1 code implementation • 3 Oct 2019 • Heng Xiao, Jin-Long Wu, Sylvain Laizet, Lian Duan
However, a major obstacle in the development of data-driven turbulence models is the lack of training data.
Fluid Dynamics
no code implementations • 13 May 2019 • Jin-Long Wu, Karthik Kashinath, Adrian Albert, Dragos Chirila, Prabhat, Heng Xiao
In this work, we present a statistical constrained generative adversarial network by enforcing constraints of covariance from the training data, which results in an improved machine-learning-based emulator to capture the statistics of the training data generated by solving fully resolved PDEs.
1 code implementation • 9 Jan 2018 • Jin-Long Wu, Heng Xiao, Eric Paterson
To this end, we present a comprehensive framework for augmenting turbulence models with physics-informed machine learning, illustrating a complete workflow from identification of input features to final prediction of mean velocities.
Fluid Dynamics 76F99
1 code implementation • 24 Jan 2017 • Jian-Xun Wang, Jin-Long Wu, Julia Ling, Gianluca Iaccarino, Heng Xiao
In this work, we introduce the procedures toward a complete PIML framework for predictive turbulence modeling, including learning Reynolds stress discrepancy function, predicting Reynolds stresses in different flows, and propagating to mean flow fields.
Fluid Dynamics