Pages that link to "Item:Q1744192"
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The following pages link to The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems (Q1744192):
Displaying 26 items.
- Generative downscaling of PDE solvers with physics-guided diffusion models (Q6639511) (← links)
- Deep adaptive sampling for surrogate modeling without labeled data (Q6639518) (← links)
- Physics-aware neural implicit solvers for multiscale, parametric PDEs with applications in heterogeneous media (Q6641874) (← links)
- Vanilla feedforward neural networks as a discretization of dynamical systems (Q6645925) (← links)
- Overcoming the curse of dimensionality in the numerical approximation of high-dimensional semilinear elliptic partial differential equations (Q6645961) (← links)
- Solutions to elliptic and parabolic problems via finite difference based unsupervised small linear convolutional neural networks (Q6647580) (← links)
- Computing ground states of Bose-Einstein condensation by normalized deep neural network (Q6648395) (← links)
- Error analysis of kernel/GP methods for nonlinear and parametric PDEs (Q6648398) (← links)
- Improved physics-informed neural networks for the reinterpreted discrete fracture model (Q6648401) (← links)
- An analysis and solution of ill-conditioning in physics-informed neural networks (Q6648404) (← links)
- Deep neural network for solving differential equations motivated by Legendre-Galerkin approximation (Q6648518) (← links)
- Deep surrogate model for learning Green's function associated with linear reaction-diffusion operator (Q6648520) (← links)
- The ADMM-PINNs algorithmic framework for nonsmooth PDE-constrained optimization: a deep learning approach (Q6649881) (← links)
- Solving PDEs on spheres with physics-informed convolutional neural networks (Q6652574) (← links)
- Improving weak PINNs for hyperbolic conservation laws: dual norm computation, boundary conditions and systems (Q6658817) (← links)
- A finite expression method for solving high-dimensional committor problems (Q6660356) (← links)
- Analysis of deep Ritz methods for semilinear elliptic equations (Q6662390) (← links)
- Error analysis of the mixed residual method for elliptic equations (Q6662404) (← links)
- Error analysis for empirical risk minimization over clipped ReLU networks in solving linear Kolmogorov partial differential equations (Q6662424) (← links)
- A stabilized physics informed neural networks method for wave equations (Q6662428) (← links)
- Deep mixed residual method for solving PDE-constrained optimization problems (Q6663447) (← links)
- Homotopy relaxation training algorithms for infinite-width two-layer ReLU neural networks (Q6665313) (← links)
- A deep learning method for incompressible fluid equations and the coupling problems (Q6665361) (← links)
- From obstacle problems to neural insights: feedforward neural network modeling of ice thickness (Q6670343) (← links)
- Mini-workshop: Mathematics of entropic AI in the natural sciences. Abstracts from the mini-workshop held April 7--12, 2024 (Q6671615) (← links)
- Adaptive deep density approximation for stochastic dynamical systems (Q6671865) (← links)
