Pages that link to "Item:Q2374961"
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The following pages link to A paradigm for data-driven predictive modeling using field inversion and machine learning (Q2374961):
Displaying 25 items.
- Unsupervised deep learning for super-resolution reconstruction of turbulence (Q5145486) (← links)
- Optimal Closures in a Simple Model for Turbulent Flows (Q5216789) (← links)
- A neural network approach for the blind deconvolution of turbulent flows (Q5231546) (← links)
- Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned (Q5235562) (← links)
- Reynolds averaged turbulence modelling using deep neural networks with embedded invariance (Q5360504) (← links)
- Data-Driven Reduced Model Construction with Time-Domain Loewner Models (Q5364198) (← links)
- Reconstruction of three-dimensional turbulent flow structures using surface measurements for free-surface flows based on a convolutional neural network (Q5884992) (← links)
- Comparison of different data-assimilation approaches to augment RANS turbulence models (Q6060768) (← links)
- A perspective on machine learning methods in turbulence modeling (Q6068270) (← links)
- Variational multiscale super‐resolution: A data‐driven approach for reconstruction and predictive modeling of unresolved physics (Q6082595) (← links)
- Physics-agnostic and physics-infused machine learning for thin films flows: modelling, and predictions from small data (Q6086911) (← links)
- Feature importance in neural networks as a means of interpretation for data-driven turbulence models (Q6095918) (← links)
- Learning black- and gray-box chemotactic PDEs/closures from agent based Monte Carlo simulation data (Q6110192) (← links)
- Deep learning closure models for large-eddy simulation of flows around bluff bodies (Q6114185) (← links)
- Space-dependent turbulence model aggregation using machine learning (Q6119255) (← links)
- A highly accurate strategy for data-driven turbulence modeling (Q6125393) (← links)
- Machine learning for RANS turbulence modeling of variable property flows (Q6158535) (← links)
- On the improvement of the extrapolation capability of an iterative machine-learning based RANS framework (Q6158562) (← links)
- Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets (Q6159313) (← links)
- Surrogate based optimization approach for the calibration of cavitation models (Q6159501) (← links)
- A non-intrusive approach for physics-constrained learning with application to fuel cell modeling (Q6164271) (← links)
- Distributed PINN for Linear Elasticity — A Unified Approach for Smooth, Singular, Compressible and Incompressible Media (Q6172992) (← links)
- A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty (Q6553801) (← links)
- Binary structured physics-informed neural networks for solving equations with rapidly changing solutions (Q6615737) (← links)
- Linear stability analysis of turbulent mean flows based on a data-consistent Reynolds-averaged Navier-Stokes model: prediction of three-dimensional stall cells around an airfoil (Q6661474) (← links)
