Pages that link to "Item:Q6097587"
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The following pages link to Physics-informed deep learning for simultaneous surrogate modeling and PDE-constrained optimization of an airfoil geometry (Q6097587):
Displaying 8 items.
- Output-based adaptive aerodynamic simulations using convolutional neural networks (Q2245362) (← links)
- A deep learning approach for the transonic flow field predictions around airfoils (Q2670066) (← links)
- Physics-informed machine learning for surrogate modeling of wind pressure and optimization of pressure sensor placement (Q6044216) (← links)
- Handling noise and overfitting in surrogate models based on non-uniform rational basis spline entities (Q6497149) (← links)
- Deep learning in computational mechanics: a review (Q6604128) (← links)
- Solving high-dimensional parametric engineering problems for inviscid flow around airfoils based on physics-informed neural networks (Q6615001) (← links)
- An analysis and solution of ill-conditioning in physics-informed neural networks (Q6648404) (← links)
- The ADMM-PINNs algorithmic framework for nonsmooth PDE-constrained optimization: a deep learning approach (Q6649881) (← links)
