Taylor series error correction network for super-resolution of discretized partial differential equation solutions

Numerical and other methods in solid mechanics (74Sxx) Artificial intelligence (68Txx) Numerical methods for partial differential equations, boundary value problems (65Nxx)

Cites Work

Automated solution of differential equations by the finite element method. The FEniCS book
The HyperCASL algorithm: a new approach to the numerical simulation of geophysical flows
Comparison of computational codes for direct numerical simulations of turbulent Rayleigh-Bénard convection
Numerical approach for MHD Al\(_2\)O\(_3\)-water nanofluid transportation inside a permeable medium using innovative computer method
A physics-informed operator regression framework for extracting data-driven continuum models
A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems
B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data
Aerodynamic design optimization: challenges and perspectives
Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems
Adversarial uncertainty quantification in physics-informed neural networks
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems
Computational mechanics enhanced by deep learning
An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications
Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Data-driven computational mechanics
Bayesian physics informed neural networks for real-world nonlinear dynamical systems
Relaxation system based sub-grid scale modelling for large eddy simulation of Burgers' equation
DOLFIN
Gmsh: A 3-D finite element mesh generator with built-in pre- and post-processing facilities
On the characterization of grid density in grid refinement studies for discretization error estimation
Finite Element Analysis
Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations
PhySR: physics-informed deep super-resolution for spatiotemporal data
Identification of vortex in unstructured mesh with graph neural networks
Multiscale graph neural networks with adaptive mesh refinement for accelerating mesh-based simulations
A deep learning -- genetic algorithm approach for aerodynamic inverse design via optimization of pressure distribution

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