NSF-funded project to develop probabilistic scientific machine learning – TAMIDS Scientific Machine Learning Lab

Across engineering and scientific disciplines, machine learning is the main method for analyzing and identifying patterns in big data and making informed decisions around that data. Recently, a new area within artificial intelligence called scientific machine learning has emerged, which introduces physics laws into machine learning models. Scientific machine learning combines the areas of artificial intelligence and scientific computation. Because scientific machine learning algorithms are informed and constrained by physics laws, they do not rely only on data and can even make predictions where there is no data. However, there has been little work on probabilistic methods in scientific machine learning, meaning that current algorithms cannot model uncertainty in the data or the physics.