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Paul Escapil-Inchauspé's GitHub 👋 🇫🇷

PhD in Engineering Sciences (2017-2021). Postdoc at UAI (2021-2023) 🇨🇱.

I am working at Data Obervatory as a Data Science Engineer and Associate Researcher.

My research interests include:

  • High Performance Computing
  • Physics-Informed Neural Networks (PINNs);
  • Operator Learning;
  • Uncertainty Quantification (UQ);
  • Iterative Solvers;
  • Finite and Boundary Element Methods.

pescap's Projects

b-pinns icon b-pinns

Pytorch implementation of Bayesian physics-informed neural networks

bempp-uq icon bempp-uq

A Python library for shape UQ based on Bempp 3.3.4

cald icon cald

Preconditioning for the EFIE

community icon community

Stores documents used by the TensorFlow developer community

covid-19 icon covid-19

Somos un grupo de voluntarios. Queremos ser la plataforma de mayor cantidad de datos y herramientas de análisis de datos relativas al COVID-19 en Chile.

covid19 icon covid19

an interactive, animated COVID-19 coronavirus map to track the outbreak over time by country and by region for selected countries

deepxde icon deepxde

A library for scientific machine learning and physics-informed learning

easyhpc icon easyhpc

A practical introduction to High Performance Computing (HPC)

hpomax icon hpomax

Easy-to-use hyper-parameters optimization environment for Deep Learning simulations

pbj icon pbj

Bempp and Jupyter based Poisson-Boltzmann electrostatics solver

pyamg icon pyamg

Algebraic Multigrid Solvers in Python

scattpinns icon scattpinns

Inverse and forward scattering problems via PINNs

scipy icon scipy

SciPy library main repository

shift icon shift

Shift regularity theorems and shape differentiability results

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