Pages that link to "Item:Q2072449"
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The following pages link to Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks (Q2072449):
Displaying 41 items.
- Mosaic flows: a transferable deep learning framework for solving PDEs on unseen domains (Q2072515) (← links)
- CENN: conservative energy method based on neural networks with subdomains for solving variational problems involving heterogeneous and complex geometries (Q2083124) (← links)
- Solving PDEs by variational physics-informed neural networks: an a posteriori error analysis (Q2084593) (← links)
- A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method (Q2096848) (← links)
- Physics-informed neural networks for shell structures (Q2102673) (← links)
- Physics-informed neural networks for gravity field modeling of small bodies (Q2104214) (← links)
- A method for representing periodic functions and enforcing exactly periodic boundary conditions with deep neural networks (Q2122243) (← links)
- Variational physics informed neural networks: the role of quadratures and test functions (Q2162334) (← links)
- Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios (Q2683433) (← links)
- Solving free-surface problems for non-shallow water using boundary and initial conditions-free physics-informed neural network (bif-PINN) (Q2687566) (← links)
- Continuous gap contact formulation based on the screened Poisson equation (Q6084767) (← links)
- Adaptive deep density approximation for fractional Fokker-Planck equations (Q6087826) (← links)
- BINN: a deep learning approach for computational mechanics problems based on boundary integral equations (Q6094674) (← links)
- A structure-preserving neural differential operator with embedded Hamiltonian constraints for modeling structural dynamics (Q6109265) (← links)
- Finite element interpolated neural networks for solving forward and inverse problems (Q6118570) (← links)
- Adaptive task decomposition physics-informed neural networks (Q6120149) (← links)
- Automatic boundary fitting framework of boundary dependent physics-informed neural network solving partial differential equation with complex boundary conditions (Q6171169) (← links)
- Exact Dirichlet boundary physics-informed neural network EPINN for solid mechanics (Q6171233) (← links)
- Data-driven nonparametric identification of material behavior based on physics-informed neural network with full-field data (Q6201157) (← links)
- Physical informed neural networks with soft and hard boundary constraints for solving advection-diffusion equations using Fourier expansions (Q6202605) (← links)
- Learning domain-independent Green's function for elliptic partial differential equations (Q6202964) (← links)
- Residual-based attention in physics-informed neural networks (Q6202991) (← links)
- Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions (Q6204733) (← links)
- Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks (Q6365490) (← links)
- Adaptive quadratures for nonlinear approximation of low-dimensional PDEs using smooth neural networks (Q6494202) (← links)
- Zero coordinate shift: whetted automatic differentiation for physics-informed operator learning (Q6497254) (← links)
- Constraint free physics-informed machine learning for micromagnetic energy minimization (Q6543808) (← links)
- A nonlocal energy-informed neural network for peridynamic correspondence material models (Q6545792) (← links)
- Gradient enhanced physics-informed neural network for iterative form-finding of tensile membrane structures by potential energy minimization (Q6558171) (← links)
- Solving Poisson problems in polygonal domains with singularity enriched physics informed neural networks (Q6585303) (← links)
- Domain decomposition learning methods for solving elliptic problems (Q6585310) (← links)
- Variational temporal convolutional networks for I-FENN thermoelasticity (Q6588274) (← links)
- Extremization to fine tune physics informed neural networks for solving boundary value problems (Q6591803) (← links)
- Exact enforcement of temporal continuity in sequential physics-informed neural networks (Q6595862) (← links)
- Transfer learning enhanced nonlocal energy-informed neural network for quasi-static fracture in rock-like materials (Q6595896) (← links)
- Physics-informed discretization-independent deep compositional operator network (Q6609787) (← links)
- Hybrid modeling design patterns (Q6617841) (← links)
- Enhancing training of physics-informed neural networks using domain decomposition-based preconditioning strategies (Q6623675) (← links)
- MHDnet: physics-preserving learning for solving magnetohydrodynamics problems (Q6646462) (← links)
- WAN discretization of PDEs: best approximation, stabilization, and essential boundary conditions (Q6660351) (← links)
- I-FENN for thermoelasticity based on physics-informed temporal convolutional network (PI-TCN) (Q6661937) (← links)
