Machine learning-driven models have achieved spectacular success in commercial applications such as language translation, speech and face recognition and bioinformatics. The natural question to ask then is: Can we bypass the traditional ways of intuition/hypothesis-driven model creation and instead use data to generate predictions of complex physics? This talk will begin with a discussion of the challenges of extending direct machine learning techniques to the prediction of physical phenomena.
