Name: Francesco Martinuzzi
Type: User
Company: Leipzig University
Bio: PhD student in Physics and Earth Sciences at Leipzig University | Universitat de València | ELLIS Society
Twitter: MartinuzziFra
Location: Leipzig, Germany
Blog: https://martinuzzifrancesco.github.io/
Francesco Martinuzzi's Projects
Material for the advanced programming course 2018-19
Code for the Advanced Programming exam
A curated list of Awesome Spectral Indices (ASI) resources
A curated list of awesome Scientific Machine Learning (SciML) papers, resources and software
A ready-to-use curated list of Spectral Indices for Remote Sensing applications.
Cellular automata creation and analysis tools
A common solve function for scientific machine learning (SciML) and beyond
Estimators for probabilities, entropies, and other complexity measures derived from observations in the context of nonlinear dynamics and complex systems
Code and homeworks for the Computational Physics course
A library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)
Award winning software library for nonlinear dynamics
Definition of dynamical systems and integrators for DynamicalSystems.jl
More than a hundred strange attractors
Shared ESDL community code
Julia interface for Reading from the Earth System Datacube
The official registry of general Julia packages
LIBSVM bindings for Julia
LinearSolve.jl: High-Performance Unified Linear Solvers
Explicitly Parameterized Neural Networks in Julia
LuxCore.jl defines the abstract layers for Lux. Allows users to be compatible with the entirely of Lux.jl without having such a heavy dependency.
readme
Personal website at https://martinuzzifrancesco.github.io/
Generalized Linear Regressions Models (penalized regressions, robust regressions, ...)
A modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
Julia (Flux) implementation of NBeats
High performance differential equation solvers for ordinary differential equations, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
Collection of predefined dynamical systems for DynamicalSystems.jl
Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications
Scripts for the examples in the ReservoirComputing.jl documentation
Reservoir computing utilities
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent