Francesco MartinuzziUniversity of Leipzig · Institute of Geophysics and Geology
Francesco Martinuzzi
Master of Science
About
7
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30
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Introduction
I am a PhD student in Physics and Earth Sciences at Leipzig University in Germany. I am under the supervision of Prof. Miguel D. Mahecha and Dr. Karin Mora at the Remote Sensing Centre for Earth System Research RSC4Earth. My research is kindly funded by the Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI. In my PhD project I explore the consequences of extreme events on the environment using Machine Learning models and Dynamical Systems theory.
Skills and Expertise
Publications
Publications (7)
Progress in Earth system science is accelerating rapidly, due to the increasing availability of multivariate datasets, often global, with moderate to high spatio-temporal resolutions. Turning these data into knowledge presents interoperability, technical, analytical, and other challenges. Earth System Data Cubes (ESDCs) have surfaced as essential t...
Spectral Indices derived from multispectral remote sensing products are extensively used to monitor Earth system dynamics (e.g. vegetation dynamics, water bodies, fire regimes). The rapid increase of proposed spectral indices led to a high demand for catalogues of spectral indices and tools for their computation. However, most of these resources ar...
We introduce ReservoirComputing.jl, an open source Julia library for reservoir computing models. It is designed for temporal or sequential tasks such as time series prediction and modeling complex dynamical systems. As such it is suited to process a range of complex spatio-temporal data sets, from mathematical models to climate data. The key ideas...
We introduce ReservoirComputing.jl, an open source Julia library for reservoir computing models. The software offers a great number of algorithms presented in the literature, and allows to expand on them with both internal and external tools in a simple way. The implementation is highly modular, fast and comes with a comprehensive documentation, wh...
In this paper we introduce JuliaSim, a high-performance programming environment designed to blend traditional modeling and simulation with machine learning. JuliaSim can build accelerated surrogates from component-based models, such as those conforming to the FMI standard, using continuous-time echo state networks (CTESN). The foundation of this en...
In this paper we introduce JuliaSim, a high-performance programming environment designed to blend traditional modeling and simulation with machine learning. JuliaSim can build accelerated surrogates from component-based models, such as those conforming to the FMI standard, using continuous-time echo state networks (CTESN). The foundation of this en...
