A novel physics-informed deep learning strategy with local time-updating discrete scheme for multi-dimensional forward and inverse consolidation problems

Cites Work

Unnamed Item
Hidden physics models: machine learning of nonlinear partial differential equations
Multilayer feedforward networks are universal approximators
Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training
Numerical bifurcation analysis of PDEs from lattice Boltzmann model simulations: a parsimonious machine learning approach
Physics-informed neural networks for high-speed flows
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
Numerical solution and bifurcation analysis of nonlinear partial differential equations with extreme learning machines
Extreme learning machine collocation for the numerical solution of elliptic PDEs with sharp gradients
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
Remark on “algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound constrained optimization”
Algorithm 778: L-BFGS-B
Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs
Physics-informed deep learning for melting heat transfer analysis with model-based transfer learning
Learning black- and gray-box chemotactic PDEs/closures from agent based Monte Carlo simulation data
Physics-informed deep learning for three-dimensional transient heat transfer analysis of functionally graded materials
Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks

This page was built for publication: A novel physics-informed deep learning strategy with local time-updating discrete scheme for multi-dimensional forward and inverse consolidation problems