linear-elasticity

CalculiX examples by Prof. Martin Kraska from Brandenburg University of Applied Sciences. Excellent starting point to master parametric modelling with CGX and CCX.

Using Physics-Informed Deep Learning (PIDL) techniques (W-PINNs-DE & W-PINNs) to solve forward and inverse hydrodynamic shock-tube problems and plane stress linear elasticity boundary value problems

LETO is a new hybrid Lagrangian-Eulerian method for topology optimization. It transfers density information from movable Lagrangian carriers to a fixed set of Eulerian quadratures and solves force equilibrium with MPM. With carrier particles as design variables, LETO reparameterizes the Eulerian solution space in a Lagrangian view.

A Matlab program which solves a static nonlinear FEM problem with linear shape functions, for an ideal elastic-plastic material

Finite element modeling for linear elasticity problem in 3D by using FEniCS software

(partial, unofficial) JAX-implementation for "Physics-informed Neural Networks for shell structures."

Simple implementation of finite element method

Code TMA4900 Industrial Mathematics, Master’s Thesis

Calculate linear elasticity for some crystals

Code for building a reduced-order model for the linear elasticity equation on a square in 2D for my specialization Project at NTNU.

Wolfram language coding examples

falcon-MATLAB is a finite element analysis program from the falcon numerical analyses suite.

PhD thesis codebase from ~2018 that implements distributed 3D FEM, random field models, uncertainty quantification.

Inverse Elasticity Physic-informed Neural Network (IE-PINN)

ORFAS — Open Rust Framework Architecture for Simulation

Tools for computing the mechanical properties of crystal structures.

An implementation of the numerical algorithm for the bound constraints optimal control problems in linear elasticity

Lagkage is a library solving for responses in a layered elastic half-space due to surface-acting normal and unidirectional shear loads, accounting for variable interlayer bond conditions.

Comparison of FEniCSx finite elements and DeepXDE physics-informed neural networks for tumor-induced elastic membrane deformation.

Prediction study of linear-eleasticity (2-D) in Steel thorough physics informed neural network.