Pages that link to "Item:Q3389009"
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The following pages link to Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks (Q3389009):
Displaying 24 items.
- Parallel physics-informed neural networks via domain decomposition (Q2133497) (← links)
- A general neural particle method for hydrodynamics modeling (Q2138776) (← links)
- Physics-informed neural networks for inverse problems in supersonic flows (Q2157127) (← links)
- A deep learning energy method for hyperelasticity and viscoelasticity (Q2671703) (← links)
- ModalPINN: an extension of physics-informed neural networks with enforced truncated Fourier decomposition for periodic flow reconstruction using a limited number of imperfect sensors (Q2672754) (← links)
- PDE-constrained models with neural network terms: optimization and global convergence (Q2699336) (← links)
- Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations (Q5023414) (← links)
- When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization? (Q5043367) (← links)
- One-dimensional ice shelf hardness inversion: clustering behavior and collocation resampling in physics-informed neural networks (Q6054214) (← links)
- Linearized Learning with Multiscale Deep Neural Networks for Stationary Navier-Stokes Equations with Oscillatory Solutions (Q6069455) (← links)
- On the use of neural networks for full waveform inversion (Q6096500) (← links)
- Physics-informed radial basis network (PIRBN): a local approximating neural network for solving nonlinear partial differential equations (Q6096508) (← links)
- A method for computing inverse parametric PDE problems with random-weight neural networks (Q6107102) (← links)
- NeuralUQ: A Comprehensive Library for Uncertainty Quantification in Neural Differential Equations and Operators (Q6154538) (← links)
- Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations (Q6171723) (← links)
- Meshless physics-informed deep learning method for three-dimensional solid mechanics (Q6554056) (← links)
- An introduction to programming physics-informed neural network-based computational solid mechanics (Q6564385) (← links)
- Applications of finite difference-based physics-informed neural networks to steady incompressible isothermal and thermal flows (Q6570872) (← links)
- Multilevel domain decomposition-based architectures for physics-informed neural networks (Q6588267) (← 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)
- Physics informed self-supervised segmentation of elastic composite materials (Q6641894) (← links)
- Filtered partial differential equations: a robust surrogate constraint in physics-informed deep learning framework (Q6645736) (← links)
- An analysis and solution of ill-conditioning in physics-informed neural networks (Q6648404) (← links)
