Pages that link to "Item:Q4294523"
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The following pages link to Neural‐network‐based approximations for solving partial differential equations (Q4294523):
Displaying 50 items.
- Deep neural network approximations for solutions of PDEs based on Monte Carlo algorithms (Q2152480) (← links)
- Solving multiscale steady radiative transfer equation using neural networks with uniform stability (Q2157930) (← links)
- Randomized Newton's method for solving differential equations based on the neural network discretization (Q2161555) (← links)
- Scientific machine learning through physics-informed neural networks: where we are and what's next (Q2162315) (← links)
- Solving flows of dynamical systems by deep neural networks and a novel deep learning algorithm (Q2168118) (← links)
- HomPINNs: Homotopy physics-informed neural networks for learning multiple solutions of nonlinear elliptic differential equations (Q2172562) (← links)
- Designing phononic crystal with anticipated band gap through a deep learning based data-driven method (Q2176922) (← links)
- ConvPDE-UQ: convolutional neural networks with quantified uncertainty for heterogeneous elliptic partial differential equations on varied domains (Q2222287) (← links)
- Collocation based training of neural ordinary differential equations (Q2236696) (← links)
- Artificial neural network approximations of Cauchy inverse problem for linear PDEs (Q2247118) (← links)
- Using Chebyshev polynomials to approximate partial differential equations (Q2268983) (← links)
- Neural network as a function approximator and its application in solving differential equations (Q2313205) (← links)
- Neural network method for solving fractional diffusion equations (Q2661030) (← 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)
- Physics and equality constrained artificial neural networks: application to forward and inverse problems with multi-fidelity data fusion (Q2671417) (← 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)
- A discontinuity capturing shallow neural network for elliptic interface problems (Q2675625) (← links)
- PI-VAE: physics-informed variational auto-encoder for stochastic differential equations (Q2679439) (← links)
- Accelerating algebraic multigrid methods via artificial neural networks (Q2679755) (← links)
- Solving non-linear Kolmogorov equations in large dimensions by using deep learning: a numerical comparison of discretization schemes (Q2680327) (← links)
- DAS-PINNs: a deep adaptive sampling method for solving high-dimensional partial differential equations (Q2681099) (← links)
- Space-time error estimates for deep neural network approximations for differential equations (Q2683168) (← links)
- Neural network architectures using min-plus algebra for solving certain high-dimensional optimal control problems and Hamilton-Jacobi PDEs (Q2683498) (← links)
- Physics-informed neural network methods based on Miura transformations and discovery of new localized wave solutions (Q2683577) (← links)
- Optimal control by deep learning techniques and its applications on epidemic models (Q2684035) (← links)
- Stochastic projection based approach for gradient free physics informed learning (Q2686876) (← links)
- An overview on deep learning-based approximation methods for partial differential equations (Q2697278) (← links)
- Greedy training algorithms for neural networks and applications to PDEs (Q2699382) (← links)
- Neural network-based variational methods for solving quadratic porous medium equations in high dimensions (Q2699489) (← links)
- BI-GreenNet: learning Green's functions by boundary integral network (Q2699491) (← links)
- Emulated digital CNN-UM solution of partial differential equations (Q3425477) (← links)
- DNN-state identification of 2D distributed parameter systems (Q4909278) (← links)
- NEURAL NETWORK-BASED DERIVATION OF EFFICIENT HIGH-ORDER RUNGE–KUTTA–NYSTRÖM PAIRS FOR THE INTEGRATION OF ORBITS (Q4914208) (← links)
- Galerkin Neural Networks: A Framework for Approximating Variational Equations with Error Control (Q5005011) (← links)
- On a multilevel Levenberg–Marquardt method for the training of artificial neural networks and its application to the solution of partial differential equations (Q5038185) (← links)
- VPVnet: A Velocity-Pressure-Vorticity Neural Network Method for the Stokes’ Equations under Reduced Regularity (Q5065192) (← links)
- An Augmented Lagrangian Deep Learning Method for Variational Problems with Essential Boundary Conditions (Q5065200) (← links)
- Approximations with deep neural networks in Sobolev time-space (Q5075578) (← links)
- Physics Informed Neural Networks (PINNs) For Approximating Nonlinear Dispersive PDEs (Q5079535) (← links)
- Adaptive Learning Neural Network Method for Solving Time–Fractional Diffusion Equations (Q5081140) (← links)
- Imaging conductivity from current density magnitude using neural networks* (Q5081798) (← links)
- Wavelet neural networks functional approximation and application (Q5097861) (← links)
- (Q5104591) (← links)
- MOD-Net: A Machine Learning Approach via Model-Operator-Data Network for Solving PDEs (Q5106291) (← links)
- DeepXDE: A Deep Learning Library for Solving Differential Equations (Q5150214) (← links)
- On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs (Q5162370) (← links)
- (Q5169152) (← links)
- fPINNs: Fractional Physics-Informed Neural Networks (Q5230662) (← links)
- (Q5294793) (← links)
- (Q5467960) (← links)
