SciML - Scientific Computing & Machine Learning
About the project
Artificial intelligence in the form of self-learning neural networks has in the recent years demonstrated extreme power and success in a series of applications such as image and sound recognition and classification as well as super-human powers in games such as chess. Nevertheless, even when a network is apparently properly trained, the networks make odd mistakes that humans would not do. Therefore, the application of such networks in mission-critical situations such as for instance, medicine and military applications or even self-driving is questionable. In contrast, within modeling based sciences such as e.g. physics, engineering and chemistry, there has been established a robust and accurate framework of analysis that often guaranties safe application with high precision. In particular, the mathematical framework developed for the analysis of partial differential equations provides a foundation for error analysis and quantification which when combined with high-performance computing enable realistic simulations of a wide-range of phenomena. In this project, we will explore ways of combining and extending traditional methods in scientific computing with neural networks in order to extend the flexibility of the traditional methods as well as increasing the robustness of today's learning techniques.