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  • 👋 Hi, I’m Jorge Urrea. A passionate for applied mathematics, who is exploring the complexity of the world and quantifying its uncertainty through physics-based and data-driven dynamical modeling.
  • 👀 I’m interested in the computational methods applied to solve Multi-physics problems either modelled by PDEs or ODEs.
  • 🌱 I’m currently learning about Physics-Informed Neural Networks (PINNs), Gaussian Processes (GPs), and Finite Element Method (FEM) technologies.
  • 💞️ I’m looking to collaborate on the development of robust/reliable libraries to computational approximate the solution of couple chemo-thermo-mechanical problems either using traditional methods or machine learning based ones.

Jorge Urrea's Projects

adaptivemivor icon adaptivemivor

MIVor: An innovative adaptive Kriging approach for efficient problem classification.

dynonet icon dynonet

"dynoNet: a neural network architecture for learning dynamical systems" by Marco Forgione and Dario Piga

hyperblog-1 icon hyperblog-1

Un blog increíble para el curso de Git y Github de Platzi

minimal icon minimal

Minimal is a Jekyll theme for GitHub Pages

pinns-tf2.0 icon pinns-tf2.0

TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).

py-pde icon py-pde

Python package for solving general partial differential equations

pybamm icon pybamm

Fast and flexible physics-based battery models in Python

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