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Found 13 papers, 9 papers with code
Frame invariance and scalability of neural operators for partial differential equations
Partial differential equations (PDEs) play a dominant role in the mathematical modeling of many complex dynamical processes.
Frame-independent vector-cloud neural network for nonlocal constitutive modeling on arbitrary grids
As such, the network can deal with any number of arbitrarily arranged grid points and thus is suitable for unstructured meshes in fluid simulations.
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.
Prediction of Reynolds Stresses in High-Mach-Number Turbulent Boundary Layers using Physics-Informed Machine Learning
This study demonstrates that the PIML approach is a computationally affordable technique for improving the accuracy of RANS-modeled Reynolds stresses for high-Mach-number turbulent flows when there is a lack of experiments and high-fidelity simulations.
BIG-bench Machine Learning
Physics-informed machine learning
Quantification of Model Uncertainty in RANS Simulations: A Review
In computational fluid dynamics simulations of industrial flows, models based on the Reynolds-averaged Navier--Stokes (RANS) equations are expected to play an important role in decades to come.
Fluid Dynamics Computational Physics
Data-Driven, Physics-Based Feature Extraction from Fluid Flow Fields
These methods are based on a physical understanding of the underlying behavior of the flow in the vicinity of the feature.
Fluid Dynamics
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
CFD-DEM Simulations of Current-Induced Dune Formation and Morphological Evolution
In this work, current-induced sediment transport problems in a wide range of regimes are simulated, including 'flat bed in motion', `small dune', `vortex dune' and suspended transport.
Fluid Dynamics
Diffusion-Based Coarse Graining in Hybrid Continuum-Discrete Solvers: Applications in CFD-DEM
Moreover, we demonstrate that the overhead computational costs incurred by the proposed coarse-graining procedure are a small portion of the total costs in typical CFD-DEM simulations as long as the number of particles per cell is reasonably large, although admittedly the computational overhead of the coarse graining often exceeds that of the CFD solver.
Computational Physics Fluid Dynamics
Diffusion-Based Coarse Graining in Hybrid Continuum-Discrete Solvers: Theoretical Formulation and A Priori Tests
The numerical tests demonstrated that the proposed coarse graining procedure based on solving diffusion equations is theoretically sound, easy to implement and parallelize in general CFD solvers, and has improved mesh-convergence characteristics compared with existing coarse graining methods.
Computational Physics
