Our most recent work on using machine learning to solve Bayesian inverse problems in computational fluid dynamics is now available online. Great job by Nikolaj Mücke from our group!
Benjamin Sanderse
CWI Amsterdam
Recent Posts
- Discretize first, filter next: model-data consistent closure models for large-eddy simulation
- Energy-conserving neural networks for turbulence closure modeling
- Stable reduced-order models for fluid flows
- Quantifying uncertainty in fatigue loads on offshore wind turbines
- Uncertainty in sloshing with new adaptive sampling techniques
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