bobbobqin Goto Github PK

2020_stressgan

adfem.jl

Innovative, efficient, and computational-graph-based finite element simulator for inverse modeling

atpinn

code

autograd

Efficiently computes derivatives of numpy code.

bobbobqin

Config files for my GitHub profile.

cann

Constitutive Artificial Neural Networks (CANNs) for modeling of hyperelastic materials

cann-1

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

cmad

Constitutive Models via Automatic Differentiation

cnn_rom-convection

geometry adaptive steady-state convection prediction

compmatbook

Computational Materials Science(Book)

data-driven-discretization-1d

Code for "Learning data-driven discretizations for partial differential equations"

databook_python

IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz

deep-flow-prediction

A framework for fluid flow (Reynolds-averaged Navier Stokes) predictions with deep learning

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

deepdistilling

Mechanistically interpretable neural networks losslessly compressed to computer code, discovering new algorithms that generalize out-of-distribution and outperform human-designed algorithms

deepmind-research

This repository contains implementations and illustrative code to accompany DeepMind publications

deeponet

Learning nonlinear operators via DeepONet

dem_for_j2_plasticity

A deep energy method (DEM) to solve J2 elastoplasticity problems in 3D.

dtaas

:factory: :left_right_arrow: :busts_in_silhouette: Digital Twin as a Service

dual-numbers-and-automatic-differentiation-using-python

Implemented the forward mode of automatic differentiation with the help of dual numbers using Python.

dual_number

Code for Automatic differentiation by using Hyper dual number

eqdiscovery

Physics-informed learning of governing equations from scarce data

f3dam

Framework for Data-Driven Design and Analysis of Materials

fcstgnn

fenics-multiphysics

fenics-tutorial

Source files and published documents for the FEniCS tutorial.

fenics.jl

A scientific machine learning (SciML) wrapper for the FEniCS Finite Element library in the Julia programming language

fenics_tutorial

FEniCS tutorial prepared for the KITP Active Matter workshop (https://www.kitp.ucsb.edu/activities/active20)

fenicssolver

multiphysics FEM solver based on Fenics library

fgn

Code repo for Fluid Graph Network