This event will be in-person and on Zoom.

Broida 1640 

Scientific Uses of Automatic Differentiation

Michael Brenner, Harvard

There is much excitement about applications of machine learning to the sciences.  Here I'm going to argue that a primary opportunity is not machine learning per se, but instead that the tools underlying the ML revolution yield significant opportunities for scientific  discovery. Primary among these tools is automatic differentiation.  I will outline a number of different directions we have been undertaking using automatic differentiation and large scale optimization to solve science problems, including developing new algorithms for solving partial differential equations, the design of energy landscapes and kinetic pathways for self assembly, and more.  My main point is to highlight opportunities and ways of thinking.

ZOOM INFO: https://ucsb.zoom.us/j/84217234934?pwd=Nk4ya1NLaWVMUVJLMEllcHJzQmRkdz09