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  • šŸ‘‹ Hi, Iā€™m Rajesh Nakka
  • šŸ‘€ Iā€™m interested in composites [especially computational] damage modelling, utilizing machine learning techniques.
  • šŸŒ± Iā€™m currently learning computational homogenization methods, computational languages Julia, Python
  • šŸ’žļø Iā€™m looking to collaborate on any projects involved in developing opensource [supporting] tools for composites damage modelling.
  • šŸ“« reach me at 338rajesh[at]gmail.com

Rajesh Nakka's Projects

calfem-python icon calfem-python

CALFEM for Python is the Python port of the CALFEM finite element toolkit. It also implements meshing function based on GMSH and triangle. Visualisation routines are implemented using visvis and matplotlib.

dl_machines icon dl_machines

Deep Learning Machines: Collection of modules used for developing deep learning models (based on tensorflow v2). It is mainly intended for my personal use but feel free to use, if you find it helpful.

docs icon docs

TensorFlow documentation

femuch.jl icon femuch.jl

Intended to develop module for Homogenization of composite materials based on FEM.

gbox icon gbox

https://gbox.readthedocs.io

gmsh.jl icon gmsh.jl

A Julia package written for conveniently installing pre-compiled official gmsh binaries, by automatically selecting binaries appropriate to your operating system.

gmshmodel icon gmshmodel

A mesh modeling interface to the Gmsh-Python-API

juliafem.jl icon juliafem.jl

The JuliaFEM software library is a framework that allows for the distributed processing of large Finite Element Models across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

keras-io icon keras-io

Keras documentation, hosted live at keras.io

makeabaqusinputfile.jl icon makeabaqusinputfile.jl

A Julia package to write ABAQUS input files for performing homogenisation of composite materials.

mpi-cnn icon mpi-cnn

The repository contains the supporting material for the work titled "A generalised deep learning-based surrogate model for homogenisation utilising material property encoding and physics-based bounds" authored by Rajesh Nakka, Dineshkumar Harursampath and Sathiskumar A Ponnusami.

pcdgan icon pcdgan

PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design

sinewaves.jl icon sinewaves.jl

Tutorial and showcase of using a binary dependency in Julia

ucplots icon ucplots

A python plotting package for unit cells and in future we are planning to include unit cell statistics as well.

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