Nowadays, machine learning (ML) is a red-hot topic which is discussed everywhere in various ways. More and more companies are relying on AI as part of their production process, be it in the domain of finance, medicine, management, art… This last application of ML algorithms, in particular, is really interesting to me. And, since the great abstract painter Cali Rezo shares this interest, we decided we would collaborate on a project to study how to apply AI to art.
In this last article from the FEM series, I present some additional concepts related to the finite element method and further developments. In particular, I focus on finite element analysis, some methods for solving time-dependent problems and optimization ideas.
Today, let’s continue with the finite element method and focus on how to implement it on a computer. How do we discretize our domain? How do we actually compute the stuff that’s in our variational formulation – efficiently, that is? How do we store and visualize our solution?
The study of partial differential equations is a fascinating field of mathematics with many concrete applications, be it in physics, mechanics, meteorology, medicine, urbanism… Mathematicians model the world as equations to better understand it and, if possible, compute a theoretical solution to a physical problem. However, we can’t always get an analytical solution – for example if the geometry is too complex – and sometimes must rely on numerical methods to get an approximation.
Last summer, I did a 2-month internship for the French startup HERETIC which has been fighting scams on the Internet for several years now. They offered me an opportunity to test my AI-engineer skills on a practical problem: how can we use machine learning to detect how fraudulent an email or a website address looks?
Last September, I had the chance to participate in an interview for the “Guide de l’Ingénieur” (i.e.: the Engineer’s guide), a special edition of the French magazine L’Usine Nouvelle that focuses on science and technology. Thanks to the journalist Christophe Bys, 3 other engineering students and I were offered the opportunity to meet with the leader of BCG Gamma, Sylvain Duranton, and ask him a few questions.
Last time, we introduced the basic concepts of our name generator and we saw how webcrawlers can help us gather information easily and efficiently. Now, it’s time to use this information to actually produce some words! To do so, we will rely on statistics and Markov chains.
Last time, I talked about cellular automata, and more precisely Conway’s Game Of Life. To continue on this topic, I searched for small applications we can derive from the concept of cellular automaton and I eventually settled for one: a RPG-like map generation algorithm.Read More »A peek at cellular automata (2)
How to accurately represent the evolution of your data over time?Read More »Data Visualisation: the Good, the Bad and the Ugly (4)