Skip to content
View GaneshShivalingappa's full-sized avatar
5 followers · 2 following
  • Karlsruhe, Germany
  • 03:16 (UTC +02:00)

Achievements

Achievement: Starstruck

Achievements

Achievement: Starstruck

Organizations

@vendy-fgg

Block or report GaneshShivalingappa

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Content in all repositories owned by your account will be closed.
Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
GaneshShivalingappa/README.md

👋 Hi, I’m @GaneshShivalingappa

🎓 Research Assistant working at the intersection of scientific computing, computational mechanics, and machine learning.

💻 I develop numerical and machine-learning methods for scientific simulation, with a focus on full-field modeling, surrogate modeling, digital material twins, uncertainty quantification, and high-performance computing.

🌟 Projects

  • 🧠 Scientific Machine Learning for Material Model Calibration
    Comparative study of Physics-Informed Neural Networks (PINNs), FEM-enhanced Neural Networks (FEM-NNs), and FEM-data-based Neural Networks (FEM-Data-NNs) for material parameter identification and full-field prediction.
    👉 Publication: DOI 10.58286/29583

  • ⚙️ FFT-Based Full-Field Modeling
    Developed and extended FFT-based full-field solvers for anisotropic elasticity, elasto-plasticity, and crystal plasticity in heterogeneous polycrystalline materials.

  • 🧩 Scientific Software & Research Code
    Contributed to scientific-computing libraries and research codebases in Julia and C++, including implementations and extensions not yet publicly released.

  • 🚀 Computational Mechanics & HPC
    Development of computational workflows for digital material twins, crystal plasticity, uncertainty quantification, and parallel scientific simulations.

🛠️ Technical Focus

Programming: Python · Julia · C++

Scientific Computing: NumPy · SciPy · FEniCS · Ferrite.jl · LinearAlgebra · StaticArrays.jl

Machine Learning: PyTorch · Physics-Informed ML · Neural Networks · Surrogate Modeling

Numerical Methods: FEM · FFT-Based Solvers · PDEs · Constitutive Modeling · Full-Field Modeling · Uncertainty Quantification

HPC & Tools: Git · Docker · Singularity · SLURM · LaTeX · ParaView

🌐 Socials:

LinkedIn

💻 Tech Stack:

Python Julia C++ NumPy PyTorch Scipy TensorFlow Matplotlib GitHub GitLab Docker LaTeX Windows Linux

📊 GitHub Stats:


Pinned Loading

  1. Parametric-NN-Models Parametric-NN-Models Public

    Physics-Informed Neural Network, Finite Element Method enhanced neural network, and FEM data-based neural network

    Python 20