Welcome to scientific-ml.com! This site aims to promote the development and mathematical theory of machine learning techniques for applications in computational science and engineering. Right now, it contains a searchable database of recent papers, links to code and software and a listing of conferences and seminars.
Machine learning techniques such as deep learning are becoming increasingly popular tools for complex problems in computational science and engineering. However, such methods are in their infancy. There are fundamental questions about their underlying mathematical theory, their potential benefits over standard techniques and their best implementation in practical scientific computing settings. This site is intended as a common resource for the communities of researchers in academia and industry who are striving to answer these questions.
The focus of this site is on scientific computing problems, rather than traditional problems in machine learning such as classification and decision-making. It also focuses primarily on methods and their mathematical theory, rather than applications to specific problems or large-scale implementations of existing algorithms.
This site is a collaborative project and we welcome your input. Please contact any of the team members if you have comments, questions or suggestions for potential content.