Scientific Machine Learning

What is Scientific Machine Learning?

Scientific machine learning (SciML) is an emerging discipline within the data science community.  SciML seeks to address domain-specic data challenges and extract insights from scientic data sets through innovative methodological solutions. SciML draws on tools from both machine learning and scientific computing to develop new methods for scalable, domain-aware, robust, reliable, and interpretable learning and data analysis, and will be critical in driving the next wave of data-driven scientific discovery in the physical and engineering sciences. 

Like scientific computing, SciML is multidisciplinary  and leverages expertise  from applied and computational mathematics, computer science, and physical science. 

Why Scientific Machine Learning?

New innovations in machine learning (ML) and "big data" are driving advances in scientific disciplines such as the Earth sciences [1], but the full potential of these techniques for data-driven discovery has yet to be fully realized. One barrier to data-driven discovery is that existing methods often do not meet the needs of scientific users. Application-agnostic algorithms, or those designed for more traditional ML applications such as image or natural language processing, can not typically be directly applied to scientic data sets and require non-trivial, task-specic modications. In other cases, the models or outputs do not provide the insights or guarantees required for scientic applications. 

Consider the following:

Research to advance data-driven discovery in the Earth and physical sciences

A non-exhaustive list of research topics in scientific machine learning: