Physics-based self-learning spiking neural network enhanced time-integration scheme for computing viscoplastic structural finite element response

Cites Work

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On a stress resultant geometrically exact shell model. I: Formulation and optimal parametrization
Finite elements based on a first-order shear deformation moderate rotation shell theory with applications to the analysis of composite structures
On the parameter identification problem for failure criteria in anisotropic bodies
Plasticity including the Bauschinger effect, studied by a neural network approach
A general deep learning framework for history-dependent response prediction based on UA-Seq2Seq model
A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
Anisotropic hyperelastic constitutive models for finite deformations combining material theory and data-driven approaches with application to cubic lattice metamaterials
Physics-based self-learning recurrent neural network enhanced time integration scheme for computing viscoplastic structural finite element response
NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations
PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training
Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
Accelerating multiscale finite element simulations of history-dependent materials using a recurrent neural network
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
Unsupervised discovery of interpretable hyperelastic constitutive laws
A nonlocal physics-informed deep learning framework using the peridynamic differential operator
Deep autoencoders for physics-constrained data-driven nonlinear materials modeling
Bayesian neural networks for uncertainty quantification in data-driven materials modeling
A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning
Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Data-driven computational mechanics
Automated discovery of generalized standard material models with EUCLID
Machine Learning for Fluid Mechanics
A Refined Small Strain and Moderate Rotation Theory of Elastic Anisotropic Shells
Accuracy and stability of integration algorithms for elastoplastic constitutive relations
Mechanics of Solid Materials
A consistent model for first-order moderate rotation plate theory
Solving high-dimensional partial differential equations using deep learning
Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
Deep learning in fluid dynamics
Data-Driven Identification of Parametric Partial Differential Equations
Intelligent stiffness computation for plate and beam structures by neural network enhanced finite element analysis
Spiking recurrent neural networks for neuromorphic computing in nonlinear structural mechanics

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