Phase field smoothing-PINN: a neural network solver for partial differential equations with discontinuous coefficients

Cites Work

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On the limited memory BFGS method for large scale optimization
A viscoelastic model for particle-reinforced composites in finite deformations
\textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition
A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
A novel sequential method to train physics informed neural networks for Allen Cahn and Cahn Hilliard equations
Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems
CENN: conservative energy method based on neural networks with subdomains for solving variational problems involving heterogeneous and complex geometries
Monte Carlo fPINNs: deep learning method for forward and inverse problems involving high dimensional fractional partial differential equations
INN: interfaced neural networks as an accessible meshless approach for solving interface PDE problems
A priori and a posteriori error estimates for the deep Ritz method applied to the Laplace and Stokes problem
An energy diminishing arbitrary Lagrangian-Eulerian finite element method for two-phase Navier-Stokes flow
Physics informed neural networks for continuum micromechanics
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
A phase field-based systematic multiscale topology optimization method for porous structures design
Physics-informed neural networks for learning the homogenized coefficients of multiscale elliptic equations
Multiscale finite volume method for finite-volume-based simulation of poroelasticity
HomPINNs: Homotopy physics-informed neural networks for learning multiple solutions of nonlinear elliptic differential equations
Physics-informed neural networks for high-speed flows
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
Micromechanical model of linear viscoelastic particle-reinforced composites with interphase
Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
A-PINN: auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations
The deep learning Galerkin method for the general Stokes equations
A discontinuity capturing shallow neural network for elliptic interface problems
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
ADLGM: an efficient adaptive sampling deep learning Galerkin method
A deep double Ritz method (\(\mathrm{D^2RM}\)) for solving partial differential equations using neural networks
Multi-scale fusion network: a new deep learning structure for elliptic interface problems
Discontinuous Spectral Element Methods for Time- and Space-Fractional Advection Equations
The Rayleigh–Ritz Method for Structural Analysis
The 2-Lagrange Multiplier Method Applied to Nonlinear Transmission Problems for the Richards Equation in Heterogeneous Soil with Cross Points
Multiscale Asymptotic Method for Maxwell's Equations in Composite Materials
Convergence of the phase field model to its sharp interface limits
Mesoscopic study of concrete I: generation of random aggregate structure and finite element mesh
A Comparison Study of Deep Galerkin Method and Deep Ritz Method for Elliptic Problems with Different Boundary Conditions
DeepXDE: A Deep Learning Library for Solving Differential Equations
Physics-Informed Neural Networks with Hard Constraints for Inverse Design
Global Regularity and Multiscale Approach for Thermal Radiation Heat Transfer
Partially Penalized Immersed Finite Element Methods For Elliptic Interface Problems
Immersed Finite Element Method for Interface Problems with Algebraic Multigrid Solver
Accuracy of High Order and Spectral Methods for Hyperbolic Conservation Laws with Discontinuous Solutions
JAX-DIPS: neural bootstrapping of finite discretization methods and application to elliptic problems with discontinuities
A cusp-capturing PINN for elliptic interface problems
Solving Elliptic Problems with Singular Sources Using Singularity Splitting Deep Ritz Method
Radial basis function neural network (RBFNN) approximation of Cauchy inverse problems of the Laplace equation
Physics-informed deep learning for melting heat transfer analysis with model-based transfer learning
Deep learning-based schemes for singularly perturbed convection-diffusion problems
Improved training of physics-informed neural networks for parabolic differential equations with sharply perturbed initial conditions
Distributed PINN for Linear Elasticity — A Unified Approach for Smooth, Singular, Compressible and Incompressible Media
Solving groundwater flow equation using physics-informed neural networks

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