Mechanical and Civil Engineering Seminar
Machine learning comprises a set of data-‐driven algorithms that can operate on big, high dimensional data sets. These algorithms have been applied with great success to a variety of applications, including e-‐commerce, finance, and image recognition. This talk will discuss how these algorithms can be applied to scientific applications, in which there are known constraints and invariance properties. This talk will focus on applications in turbulence modeling and materials science. In both of these fields, the multi-‐scale nature of the physics necessitates the use of constitutive closure models in simulations. With the increasing availability of large, high-‐fidelity data sets, there is now the possibility of using machine learning to provide more accurate closure models. A key facet of this research has been the tight integration of scientific knowledge with data-‐driven methods.
*This lecture is part of the Young Investigators Lecture Series sponsored by the Caltech Division of Engineering & Applied Science.