victorich79's Stars
lululxvi/deepxde
A library for scientific machine learning and physics-informed learning
AndreWeiner/ml-cfd-lecture
Lecture material for machine learning applied to computational fluid mechanics
ehsanhaghighat/sciann
Deep learning for Engineers - Physics Informed Deep Learning
ehsanhaghighat/sciann-applications
A place to share problems solved with SciANN
ammarkh95/ABAQUS_PDALAC
Development of the Failure Criteria for Composites using ABAQUS Subroutines (UMAT/VUMAT)
nasa/CompDam_DGD
JinshuaiBai/PINN_Comp_Mech
PINN program for computational mechanics
tdegeus/GooseFFT
Micro mechanical computations with an FFT-based method
hyoungsuksuh/ABAQUS_NN
LivingMatterLab/CANN
When using, please cite "A new family of Constitutive Artificial Neural Networks towards automated model discovery", CMAME, https://doi.org/10.1016/j.cma.2022.115731 and the relevant other papers from this repo
mimesis-inria/DeepPhysX
Interfacing AI with numerical simulation.
danpak94/Deep-Cardiac-Volumetric-Mesh
sheadan/DeepGreen
DeepGreen network written in Tensorflow 2
EndritPJ/CFD_Machine_Learning
farazarbabi/CNN_FEM
In this project I use the generated dataset of stress analysis results of FVM method to train a Convolutional Neural Netwrok (CNN) model and predict stress distribution over the plate for uncalculated forces.
Aero-tomato/SMA-UMAT
A user-defined material subroutine for polycrystalline shape memory alloys under large deformations
InstituteOfMechanics/Thermomechanical_Gradient_Enhanced_Damage_UMAT
An Abaqus framework based on user materials (Umat and Umatht) for multi-field problems. A thermo-mechanically coupled gradient-enhanced damage model is implemented.
LallyLabTCD/localBasisAbaqus
This is the supporting material for the tutorial on fibre based hyperelasticity using a local coordinate basis in Abaqus
luisez1988/Viscoplastic-SSMC
A viscoplastic strain-softening Mohr-Coulomb UMAT
GuangC-iScience/rnn-viscoelasticity
Constitutive modeling · Deep learning · History-dependent materials · Recurrent neural networks · Viscoelasticity
liangbright/pytorch_fea
shayansss/hml
Implementation of a new hybrid machine learning technique for multi-fidelity surrogates of finite elements models with applications in multi-physics modeling of soft tissues.
victorlefevre/UHYPER_Shrimali_Lefevre_Lopez-Pamies
This ABAQUS UHYPER subroutine implements the hyperelastic energy density derived in Journal of the Mechanics and Physics of Solids 122 (2019), 364–380 for the macroscopic elastic response of non-Gaussian elastomers weakened by an isotropic and non-percolative distribution of equiaxed pores. This result is valid for any choice of I1-based incompressible energy density characterizing the non-Gaussian isotropic elastic response of the underlying elastomer. The present subroutine is implemented for the choice of strain energy density proposed in Comptes Rendus Mecanique 338 (2010), 3–11.
filippo-masi/TANN-multiscale
Multiscale modeling of inelastic materials with Thermodynamics-based Artificial Neural Networks (TANN)
liangbright/DNN_FEM_Integration
saeedmhz/MultiRes-WNet
ehsanhaghighat/sciann-website
Documentation for SciANN
IsmailBerkeCam/CFD-AI
CFD Aneurysm Predictions With NN
liangbright/pytorch_fea_paper
tdegeus/GMatElastoPlasticFiniteStrainSimo
Simo elasto-plastic model