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Found 9 papers, 8 papers with code
CGNSDE: Conditional Gaussian Neural Stochastic Differential Equation for Modeling Complex Systems and Data Assimilation
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.
Learning About Structural Errors in Models of Complex Dynamical Systems
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.
Operator Learning for Continuous Spatial-Temporal Model with Gradient-Based and Derivative-Free Optimization Methods
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.
CEBoosting: Online Sparse Identification of Dynamical Systems with Regime Switching by Causation Entropy Boosting
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.
Enforcing Deterministic Constraints on Generative Adversarial Networks for Emulating Physical Systems
Recently, GANs have been used to emulate complex physical systems such as turbulent flows.
Flows Over Periodic Hills of Parameterized Geometries: A Dataset for Data-Driven Turbulence Modeling From Direct Simulations
However, a major obstacle in the development of data-driven turbulence models is the lack of training data.
Fluid Dynamics
Enforcing Statistical Constraints in Generative Adversarial Networks for Modeling Chaotic Dynamical Systems
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.
Physics-Informed Machine Learning Approach for Augmenting Turbulence Models: A Comprehensive Framework
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
A Comprehensive Physics-Informed Machine Learning Framework for Predictive Turbulence Modeling
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
