Pages that link to "Item:Q5038552"
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The following pages link to Learned turbulence modelling with differentiable fluid solvers: physics-based loss functions and optimisation horizons (Q5038552):
Displaying 12 items.
- A physics-informed learning approach to Bernoulli-type free boundary problems (Q2107176) (← links)
- Towards high-accuracy deep learning inference of compressible flows over aerofoils (Q2108599) (← links)
- Dynamic calibration of differential equations using machine learning, with application to turbulence models (Q2135788) (← links)
- Ensemble Gradient for Learning Turbulence Models from Indirect Observations (Q5065144) (← links)
- Comparison of neural closure models for discretised PDEs (Q6104834) (← links)
- Combining direct and indirect sparse data for learning generalizable turbulence models (Q6107115) (← links)
- A multifidelity deep operator network approach to closure for multiscale systems (Q6116145) (← links)
- Differentiable hybrid neural modeling for fluid-structure interaction (Q6198155) (← links)
- A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty (Q6553801) (← links)
- Differentiability in unrolled training of neural physics simulators on transient dynamics (Q6663245) (← links)
- Neural differentiable modeling with diffusion-based super-resolution for two-dimensional spatiotemporal turbulence (Q6663283) (← links)
- Super-resolution of turbulence with dynamics in the loss (Q6669442) (← links)
