Pages that link to "Item:Q2157127"
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The following pages link to Physics-informed neural networks for inverse problems in supersonic flows (Q2157127):
Displaying 30 items.
- Data-driven prediction of soliton solutions of the higher-order NLSE via the strongly-constrained PINN method (Q2107164) (← links)
- DeepM\&Mnet for hypersonics: predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators (Q2133505) (← links)
- HomPINNs: Homotopy physics-informed neural networks for learning multiple solutions of nonlinear elliptic differential equations (Q2172562) (← links)
- Physics-informed neural networks for high-speed flows (Q2175317) (← links)
- Modular machine learning-based elastoplasticity: generalization in the context of limited data (Q2693407) (← links)
- An unsupervised latent/output physics-informed convolutional-LSTM network for solving partial differential equations using peridynamic differential operator (Q2693426) (← links)
- Neural network-based modelling of subsonic cavity flows (Q5402730) (← links)
- Multi-Fidelity Machine Learning Applied to Steady Fluid Flows (Q5880416) (← links)
- A unified scalable framework for causal sweeping strategies for physics-informed neural networks (PINNs) and their temporal decompositions (Q6048429) (← links)
- Deep learning-accelerated computational framework based on physics informed neural network for the solution of linear elasticity (Q6053463) (← links)
- Physics-informed neural networks with parameter asymptotic strategy for learning singularly perturbed convection-dominated problem (Q6062204) (← links)
- Physics-Informed Neural Networks for Solving Dynamic Two-Phase Interface Problems (Q6068803) (← links)
- DeepStSNet: reconstructing the quantum state-resolved thermochemical nonequilibrium flowfield using deep neural operator learning with scarce data (Q6095078) (← links)
- A dimension-augmented physics-informed neural network (DaPINN) with high level accuracy and efficiency (Q6095102) (← links)
- On the use of neural networks for full waveform inversion (Q6096500) (← links)
- A decoupled physics-informed neural network for recovering a space-dependent force function in the wave equation from integral overdetermination data (Q6103366) (← links)
- A method for computing inverse parametric PDE problems with random-weight neural networks (Q6107102) (← links)
- PI-VEGAN: Physics Informed Variational Embedding Generative Adversarial Networks for Stochastic Differential Equations (Q6151340) (← links)
- Adaptive transfer learning for PINN (Q6173323) (← links)
- Discontinuity computing using physics-informed neural networks (Q6184289) (← links)
- Data-driven nonparametric identification of material behavior based on physics-informed neural network with full-field data (Q6201157) (← links)
- Physical informed neural networks with soft and hard boundary constraints for solving advection-diffusion equations using Fourier expansions (Q6202605) (← links)
- Physics-informed neural networks for inverse problems in supersonic flows (Q6392007) (← links)
- Roenet: predicting discontinuity of hyperbolic systems from continuous data (Q6499882) (← links)
- RiemannONets: interpretable neural operators for Riemann problems (Q6550161) (← links)
- Anti-derivatives approximator for enhancing physics-informed neural networks (Q6550163) (← links)
- The data-driven rogue waves of the Hirota equation by using mix-training PINNs approach (Q6558868) (← links)
- Energy-informed graph transformer model for solid mechanical analyses (Q6591778) (← links)
- Least-square finite difference-based physics-informed neural network for steady incompressible flows (Q6663359) (← links)
- JAX-fluids 2.0: towards HPC for differentiable CFD of compressible two-phase flows (Q6671933) (← links)
