Pages that link to "Item:Q1088526"

The following pages link to Spectral and finite difference solutions of the Burgers equations (Q1088526):

Displaying 50 items.

• Numerical approximation of partial differential equations by a variable projection method with artificial neural networks (Q2160472) (← links)
• Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems (Q2184334) (← links)
• Neural-net-induced Gaussian process regression for function approximation and PDE solution (Q2214653) (← links)
• Adaptive activation functions accelerate convergence in deep and physics-informed neural networks (Q2223034) (← links)
• Numerical study of the thermodynamic uncertainty relation for the KPZ-equation (Q2227188) (← links)
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations (Q2314336) (← links)
• A comparison of differential quadrature methods for the solution of partial differential equations (Q2368261) (← links)
• Dynamics in spectral solutions of Burgers equation (Q2372930) (← links)
• Exact calculation of Fourier series in nonconforming spectral-element methods (Q2489021) (← links)
• Geophysical-astrophysical spectral-element adaptive refinement (GASpAR): object-oriented \(h\)-adaptive fluid dynamics simulation (Q2489026) (← links)
• Simultaneous space-time adaptive wavelet solution of nonlinear parabolic differential equations (Q2489692) (← links)
• Spectral properties of Burgers and KPZ turbulence (Q2494318) (← links)
• Implementation of arbitrary inner product in the global Galerkin method for incompressible Navier-Stokes equations (Q2572214) (← links)
• Wavelet-Taylor Galerkin method for the Burgers equation (Q2583155) (← links)
• Physics-data combined machine learning for parametric reduced-order modelling of nonlinear dynamical systems in small-data regimes (Q2678495) (← links)
• A metalearning approach for physics-informed neural networks (PINNs): application to parameterized PDEs (Q2681136) (← links)
• An improved coarse-mesh nodal integral method for partial differential equations (Q3123987) (← links)
• Numerical Gaussian Processes for Time-Dependent and Nonlinear Partial Differential Equations (Q3130409) (← links)
• Spurious Behaviour of Numerically Computed Fluid Flow (Q3773608) (← links)
• (Q3782702) (← links)
• Convergence of Spectral Methods for Burgers’ Equation (Q4031681) (← links)
• Space‐time spectral element methods for unsteady convection‐diffusion problems (Q4504670) (← links)
• Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations (Q4558167) (← links)
• (Q4659196) (← links)
• On the Closed‐Form Solutions of the Wave, Diffusion and Burgers Equations in Fluid Mechanics (Q4847508) (← links)
• A high order spectral volume solution to the Burgers' equation using the Hopf–Cole transformation (Q4898009) (← links)
• EXISTENCE AND NUMERICAL APPROXIMATION OF SOLUTIONS OF AN IMPROVED INTERNAL WAVE MODEL (Q5011184) (← links)
• SOLVING NONLINEAR PDES USING THE HIGHER ORDER HAAR WAVELET METHOD ON NONUNIFORM AND ADAPTIVE GRIDS (Q5016283) (← links)
• CHOICE OF A BASIS TO SOLVE THE LANE-EMDEN EQUATION (Q5058436) (← links)
• Resource-Constrained Model Selection for Uncertainty Propagation and Data Assimilation (Q5119641) (← links)
• (Q5156807) (← links)
• Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations (Q5162369) (← links)
• Spectral properties of the scaling limit solutions of the Burger’s equation with singular data (Q5687855) (← links)
• A spectral multidomain technique for the computation of the czochralski melt configuration (Q5689581) (← links)
• Burgers equation with a fractional derivative; hereditary effects on nonlinear acoustic waves (Q5753653) (← links)
• Modified Legendre Rational Spectral Method for Burgers Equation on the Whole Line (Q5882290) (← links)
• Sparse Deep Neural Network for Nonlinear Partial Differential Equations (Q5885722) (← links)
• A fully adaptive wavelet algorithm for parabolic partial differential equations (Q5928469) (← links)
• Spectral properties of solutions of the Burgers equation with small dissipation (Q5944767) (← links)
• Connection coefficients on an interval and wavelet solutions of Burgers equation (Q5949505) (← links)
• Three ways to solve partial differential equations with neural networks — A review (Q6068232) (← links)
• Two-grid finite difference method for 1D fourth-order Sobolev-type equation with Burgers' type nonlinearity (Q6104699) (← links)
• Numerical computation of partial differential equations by hidden-layer concatenated extreme learning machine (Q6159015) (← links)
• Discovery of PDEs driven by data with sharp gradient or discontinuity (Q6161547) (← links)
• Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations (Q6171723) (← links)
• A multiresolution collocation method and its convergence for Burgers' type equations (Q6188968) (← links)
• Physics-informed machine learning method with space-time Karhunen-Loève expansions for forward and inverse partial differential equations (Q6196622) (← links)
• PDE-READ: human-readable partial differential equation discovery using deep learning (Q6488684) (← links)
• \texttt{Weak-PDE-LEARN}: a weak form based approach to discovering PDEs from noisy, limited data (Q6498485) (← links)
• Efficient Bayesian physics informed neural networks for inverse problems via ensemble Kalman inversion (Q6553819) (← links)