Pages that link to "Item:Q2002333"
From MaRDI portal
The following pages link to DGM: a deep learning algorithm for solving partial differential equations (Q2002333):
Displaying 50 items.
- Solving parametric partial differential equations with deep rectified quadratic unit neural networks (Q2103467) (← links)
- Solving partial differential equation based on extreme learning machine (Q2104376) (← links)
- Discrete gradient flow approximations of high dimensional evolution partial differential equations via deep neural networks (Q2108629) (← links)
- Uniform convergence guarantees for the deep Ritz method for nonlinear problems (Q2110466) (← links)
- On stability and regularization for data-driven solution of parabolic inverse source problems (Q2112451) (← links)
- Neural eikonal solver: improving accuracy of physics-informed neural networks for solving eikonal equation in case of caustics (Q2112483) (← links)
- A deep domain decomposition method based on Fourier features (Q2112697) (← links)
- Two neural-network-based methods for solving elliptic obstacle problems (Q2112890) (← links)
- Deep learning Gauss-Manin connections (Q2113261) (← links)
- Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation (Q2115690) (← links)
- DNN expression rate analysis of high-dimensional PDEs: application to option pricing (Q2117328) (← links)
- A theoretical analysis of deep neural networks and parametric PDEs (Q2117329) (← links)
- GINNs: graph-informed neural networks for multiscale physics (Q2120776) (← links)
- A method for representing periodic functions and enforcing exactly periodic boundary conditions with deep neural networks (Q2122243) (← links)
- An efficient neural network method with plane wave activation functions for solving Helmholtz equation (Q2122592) (← links)
- Adaptive two-layer ReLU neural network. I: Best least-squares approximation (Q2122629) (← links)
- Adaptive two-layer ReLU neural network. II: Ritz approximation to elliptic PDEs (Q2122635) (← links)
- Using deep learning to extend the range of air pollution monitoring and forecasting (Q2123348) (← links)
- Meta-learning pseudo-differential operators with deep neural networks (Q2123371) (← links)
- DPM: a deep learning PDE augmentation method with application to large-eddy simulation (Q2123852) (← links)
- On some neural network architectures that can represent viscosity solutions of certain high dimensional Hamilton-Jacobi partial differential equations (Q2123971) (← links)
- A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables (Q2124009) (← links)
- Data-driven discovery of coarse-grained equations (Q2124010) (← links)
- Structure probing neural network deflation (Q2124019) (← links)
- Symplectic neural networks in Taylor series form for Hamiltonian systems (Q2124341) (← links)
- Deep least-squares methods: an unsupervised learning-based numerical method for solving elliptic PDEs (Q2125011) (← links)
- Trend to equilibrium for the kinetic Fokker-Planck equation via the neural network approach (Q2125428) (← links)
- A derivative-free method for solving elliptic partial differential equations with deep neural networks (Q2125435) (← links)
- Int-Deep: a deep learning initialized iterative method for nonlinear problems (Q2125440) (← links)
- Deep learning of free boundary and Stefan problems (Q2128318) (← links)
- PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain (Q2128357) (← links)
- PFNN: a penalty-free neural network method for solving a class of second-order boundary-value problems on complex geometries (Q2128373) (← links)
- Solving the linear transport equation by a deep neural network approach (Q2129138) (← links)
- ISALT: inference-based schemes adaptive to large time-stepping for locally Lipschitz ergodic systems (Q2129142) (← links)
- Solving inverse-PDE problems with physics-aware neural networks (Q2129334) (← links)
- SelectNet: self-paced learning for high-dimensional partial differential equations (Q2131038) (← links)
- DeepM\&Mnet: inferring the electroconvection multiphysics fields based on operator approximation by neural networks (Q2131084) (← links)
- Weak form theory-guided neural network (TgNN-wf) for deep learning of subsurface single- and two-phase flow (Q2131089) (← links)
- On obtaining sparse semantic solutions for inverse problems, control, and neural network training (Q2132578) (← links)
- Least-squares ReLU neural network (LSNN) method for linear advection-reaction equation (Q2132582) (← links)
- Using neural networks to accelerate the solution of the Boltzmann equation (Q2132591) (← links)
- Mutual information for explainable deep learning of multiscale systems (Q2132642) (← links)
- A modified batch intrinsic plasticity method for pre-training the random coefficients of extreme learning machines (Q2133017) (← links)
- SPINN: sparse, physics-based, and partially interpretable neural networks for PDEs (Q2133032) (← links)
- Solving and learning nonlinear PDEs with Gaussian processes (Q2133484) (← links)
- Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation (Q2133495) (← links)
- Mesh-Conv: convolution operator with mesh resolution independence for flow field modeling (Q2133572) (← links)
- MIM: a deep mixed residual method for solving high-order partial differential equations (Q2133607) (← links)
- A new efficient approximation scheme for solving high-dimensional semilinear PDEs: control variate method for deep BSDE solver (Q2133701) (← links)
- Meta-mgnet: meta multigrid networks for solving parameterized partial differential equations (Q2133752) (← links)
