Wouter Edeling
9 papers 55 citations h-index 3 2019–2025
Energy-Conserving Neural Network for Turbulence Closure Modeling
In turbulence modeling, we are concerned with finding closure models that represent the effect of the subgrid scales on the resolved scales. Recent approaches gravitate towards machine learning...
Reduced Data-Driven Turbulence Closure for Capturing Long-Term Statistics
We introduce a simple, stochastic, a-posteriori, turbulence closure model based on a reduced subgrid scale term. This subgrid scale term is tailor-made to capture the statistics of a small set of...
Sensitivity Analysis of High-Dimensional Models with Correlated Inputs
Sensitivity analysis is an important tool used in many domains of computational science to either gain insight into the mathematical model and interaction of its parameters or study the...
Resampling with neural networks for stochastic parameterization in multiscale systems
In simulations of multiscale dynamical systems, not all relevant processes can be resolved explicitly. Taking the effect of the unresolved processes into account is important, which introduces the...
Introducing VECMAtk - Verification, Validation and Uncertainty Quantification for Multiscale and HPC Simulations
Automated Variance-Based Sensitivity Analysis of a Heterogeneous Atomistic-Continuum System
Energy-Conserving Neural Network Closure Model for Long-Time Accurate and Stable LES
Machine learning-based closure models for LES have shown promise in capturing complex turbulence dynamics but often suffer from instabilities and physical inconsistencies. In this work, we develop...
Modeling Advection-Dominated Flows with Space-Local Reduced-Order Models
Reduced-order models (ROMs) are often used to accelerate the simulation of large physical systems. However, traditional ROM techniques, such as those based on proper orthogonal decomposition...