Hi there.
Welcome to the AARMS Collaborative Research Group Mathematical foundations and applications of Scientific Machine Learning .
Welcome to the AARMS Collaborative Research Group Mathematical foundations and applications of Scientific Machine Learning .
Scientific Machine Learning is concerned with using methods from machine learning to tackle problems that have traditionally been investigated using classical scientific computing. Until very recently, science, and in particular scientific computing, has followed the classical formula From rules to data, meaning one first defines a mathematical theory or a computational algorithm which generates predictions (data), that is then compared to some benchmarks, such as real-world observations. Machine learning reverses the direction to From data to rules. Data is being analysed and rules are being derived from it. In other words, machine learning is devoted to learning from data.
Given the ever-increasing amount of data that has been generated over the past 50 years or so in virtually all areas of the mathematical sciences, including astronomy, biology, chemistry, geophysics, meteorology and oceanography, the increased interest in applying machine learning to all these areas is quite natural. This Collaborative Research Group will capitalize on the combined expertise of researchers in the Atlantic region to push the envelope of Scientific Machine Learning both in terms of theoretical developments and a variety of practical applications to the aforementioned fields.
We are currently organizing a biweekly online seminar on Scientific Machine Learning. The latest schedule along with the connection information can be found here: AARMS Scientific Machine Learning seminar
We also organize a hybrid 2 1/2 day workshop from June 1-3 at Memorial University of Newfoundland (and Webex). The program for this workshop can be found here: Program
If you are interested in this Collaborative Research Group and want to stay in the loop then please just send a message to Alex Bihlo, abihlo@mun.ca.