Pages that link to "Item:Q1344634"

The following pages link to Solution of nonlinear ordinary differential equations by feedforward neural networks (Q1344634):

Displaying 32 items.

• Chebyshev neural network based model for solving Lane-Emden type equations (Q297688) (← links)
• Design feed forward neural network to solve singular boundary value problems (Q469857) (← links)
• Integration modified wavelet neural networks for solving thin plate bending problem (Q727299) (← links)
• Weak adversarial networks for high-dimensional partial differential equations (Q777606) (← links)
• Numerical solution for high order differential equations using a hybrid neural network-optimization method (Q864765) (← links)
• Establishing criteria to ensure successful feedforward artificial neural network modelling of mechanical systems (Q969828) (← links)
• The numerical solution of linear ordinary differential equations by feedforward neural networks (Q1334712) (← links)
• Feedforward neural nets as discretization schemes for ODEs and DAEs (Q1372058) (← links)
• Approximation properties of local bases assembled from neural network transfer functions (Q1596850) (← links)
• A neural computational intelligence method based on Legendre polynomials for fuzzy fractional order differential equation (Q1670330) (← links)
• A novel improved extreme learning machine algorithm in solving ordinary differential equations by Legendre neural network methods (Q1716363) (← links)
• Orthonormal Bernoulli wavelets neural network method and its application in astrophysics (Q1983927) (← links)
• Numerical simulation of Volterra-Fredholm integral equations using least squares support vector regression (Q2052276) (← links)
• Int-Deep: a deep learning initialized iterative method for nonlinear problems (Q2125440) (← links)
• CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method (Q2142144) (← links)
• An investigation of approximate solutions for second order ordinary differential equations using sigmoid-weighted neural networks (Q2144766) (← links)
• Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks (Q2222972) (← links)
• On computing the hyperparameter of extreme learning machines: algorithm and application to computational PDEs, and comparison with classical and high-order finite elements (Q2671403) (← links)
• Solving non-linear Kolmogorov equations in large dimensions by using deep learning: a numerical comparison of discretization schemes (Q2680327) (← links)
• Isogeometric neural networks: a new deep learning approach for solving parameterized partial differential equations (Q2683423) (← links)
• An efficient numerical method to solve ordinary differential equations using Fibonacci neural networks (Q2686539) (← links)
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• Pseudospectral method for second-order autonomous nonlinear differential equations (Q5114305) (← links)
• Legendre Neural Network for Solving Linear Variable Coefficients Delay Differential-Algebraic Equations with Weak Discontinuities (Q5157033) (← links)
• Neuro-optimized numerical treatment of HIV infection model (Q5164567) (← links)
• Hermite Functional Link Neural Network for Solving the Van der Pol–Duffing Oscillator Equation (Q5380555) (← links)
• BINN: a deep learning approach for computational mechanics problems based on boundary integral equations (Q6094674) (← links)
• Physics-informed ConvNet: learning physical field from a shallow neural network (Q6199712) (← links)
• Physics-informed neural networks and functional interpolation for stiff chemical kinetics (Q6565148) (← links)
• Machine learning approaches for the solution of the Riemann problem in fluid dynamics: a case study (Q6593781) (← links)
• Solving distributed-order fractional equations by LS-SVR (Q6606437) (← links)