A short introduction to Neural Networks and their application to Earth and Materials Science Science

Autor:	Fanelli, Duccio, Bindi, Luca, Chicchi, Lorenzo, Pereti, Claudio, Sessoli, Roberta, Tommasini, Simone
Rok vydání:	2024
Předmět:	Physics - Data Analysis Statistics and Probability Condensed Matter - Disordered Systems and Neural Networks Condensed Matter - Materials Science Physics - Geophysics
Druh dokumentu:	Working Paper
Popis:	Neural networks are gaining widespread relevance for their versatility, holding the promise to yield a significant methodological shift in different domain of applied research. Here, we provide a simple pedagogical account of the basic functioning of a feedforward neural network. Then we move forward to reviewing two recent applications of machine learning to Earth and Materials Science. We will in particular begin by discussing a neural network based geothermobarometer, which returns reliable predictions of the pressure/temperature conditions of magma storage. Further, we will turn to illustrate how machine learning tools, tested on the list of minerals from the International Mineralogical Association, can help in the search for novel superconducting materials.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2408.11395 Zobrazit plný text záznamu View this record from Arxiv