Pages that link to "Item:Q2175317"

The following pages link to Physics-informed neural networks for high-speed flows (Q2175317):

Displaying 50 items.

• Prediction and identification of physical systems by means of physically-guided neural networks with meaningful internal layers (Q2236964) (← links)
• ReF-nets: physics-informed neural network for Reynolds equation of gas bearing (Q2670343) (← links)
• A finite element based deep learning solver for parametric PDEs (Q2670366) (← links)
• A-PINN: auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations (Q2671335) (← 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)
• Improved deep neural networks with domain decomposition in solving partial differential equations (Q2674166) (← links)
• Kolmogorov n-width and Lagrangian physics-informed neural networks: a causality-conforming manifold for convection-dominated PDEs (Q2678525) (← links)
• Data driven modeling of interfacial traction-separation relations using a thermodynamically consistent neural network (Q2678537) (← links)
• A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks (Q2679440) (← links)
• Isogeometric analysis-based physics-informed graph neural network for studying traffic jam in neurons (Q2679502) (← links)
• Physics-informed neural networks combined with polynomial interpolation to solve nonlinear partial differential equations (Q2682670) (← links)
• Surrogate modeling for Bayesian inverse problems based on physics-informed neural networks (Q2683056) (← links)
• Physics-informed neural network methods based on Miura transformations and discovery of new localized wave solutions (Q2683577) (← links)
• Data-driven forward-inverse problems for Yajima-Oikawa system using deep learning with parameter regularization (Q2684140) (← links)
• Time difference physics-informed neural network for fractional water wave models (Q2690093) (← links)
• An unsupervised latent/output physics-informed convolutional-LSTM network for solving partial differential equations using peridynamic differential operator (Q2693426) (← links)
• An overview on deep learning-based approximation methods for partial differential equations (Q2697278) (← links)
• Physics informed neural networks: a case study for gas transport problems (Q2699348) (← links)
• Greedy training algorithms for neural networks and applications to PDEs (Q2699382) (← links)
• Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks (Q3389009) (← links)
• DIFFUSION ON FRACTAL OBJECTS MODELING AND ITS PHYSICS-INFORMED NEURAL NETWORK SOLUTION (Q5024806) (← links)
• Physics Informed Neural Networks (PINNs) For Approximating Nonlinear Dispersive PDEs (Q5079535) (← links)
• DeepXDE: A Deep Learning Library for Solving Differential Equations (Q5150214) (← links)
• Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations (Q5162369) (← links)
• On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs (Q5162370) (← links)
• Neural network-based modelling of subsonic cavity flows (Q5402730) (← links)
• Multi-Fidelity Machine Learning Applied to Steady Fluid Flows (Q5880416) (← links)
• A machine learning approach to calculating the non-equilibrium diffusion coefficients in the state-to-state solution of the Navier-Stokes equations (Q6040353) (← links)
• Physics-informed neural networks for the Reynolds-averaged Navier-Stokes modeling of Rayleigh-Taylor turbulent mixing (Q6060732) (← links)
• Physics-informed neural networks with parameter asymptotic strategy for learning singularly perturbed convection-dominated problem (Q6062204) (← links)
• Three ways to solve partial differential equations with neural networks — A review (Q6068232) (← links)
• CoolPINNs: a physics-informed neural network modeling of active cooling in vascular systems (Q6072827) (← links)
• A stepwise physics‐informed neural network for solving large deformation problems of hypoelastic materials (Q6082603) (← links)
• Fluid Flow Modelling Using Physics-Informed Convolutional Neural Network in Parametrised Domains (Q6092912) (← links)
• Unsteady reduced order model with neural networks and flight-physics-based regularization for aerodynamic applications (Q6093461) (← links)
• BINN: a deep learning approach for computational mechanics problems based on boundary integral equations (Q6094674) (← links)
• Single-track thermal analysis of laser powder bed fusion process: parametric solution through physics-informed neural networks (Q6094682) (← links)
• Fully probabilistic deep models for forward and inverse problems in parametric PDEs (Q6095115) (← links)
• On the use of neural networks for full waveform inversion (Q6096500) (← links)
• Complex dynamics on the one-dimensional quantum droplets via time piecewise PINNs (Q6096531) (← links)
• FluxNet: a physics-informed learning-based Riemann solver for transcritical flows with non-ideal thermodynamics (Q6097610) (← links)
• Solving multi-material problems in solid mechanics using physics-informed neural networks based on domain decomposition technology (Q6099225) (← links)
• Data-driven reduced order modelling for patient-specific hemodynamics of coronary artery bypass grafts with physical and geometrical parameters (Q6101879) (← links)
• A decoupled physics-informed neural network for recovering a space-dependent force function in the wave equation from integral overdetermination data (Q6103366) (← links)
• Learning high frequency data via the coupled frequency predictor-corrector triangular DNN (Q6104304) (← links)
• A method for computing inverse parametric PDE problems with random-weight neural networks (Q6107102) (← links)
• Predicting continuum breakdown with deep neural networks (Q6107123) (← links)
• Error estimates and physics informed augmentation of neural networks for thermally coupled incompressible Navier Stokes equations (Q6109270) (← links)
• Investigating and Mitigating Failure Modes in Physics-Informed Neural Networks (PINNs) (Q6111307) (← links)
• Error convergence and engineering-guided hyperparameter search of PINNs: towards optimized I-FENN performance (Q6116144) (← links)