Deniz Oktay will present his FPO "Translating Between Scientific Computing and Machine Learning with Automatic Differentiation" on Tuesday, June 18, 2024 at 1:00 PM in CS 401.
Location: CS 401
The members of Deniz’ committee are as follows:
Examiners: Ryan Adams (Adviser), Benjamin Eysenbach, Sigrid Adriaenssens
Readers: Elad Hazan, Szymon Rusinkiewicz
A copy of his thesis is available upon request. Please email gradinfo@cs.princeton.edu if you would like a copy of the thesis.
Everyone is invited to attend his talk.
Abstract follows below:
Scientific computing and machine learning, although historically separate fields, have seen much effort in unification as of recent years, especially as machine learning techniques have shown promise in scientific problems. In this thesis, I present work in the intersection of these areas, using automatic differentiation (AD) as the common language between the two. First, I present a methodological advancement in AD: Randomized Automatic Differentiation, a technique to reduce the memory usage of AD, and show that it can provide memory improvements in both machine learning and scientific computing applications.
Next, I focus on mechanical design. I first describe Varmint: A Variational Material Integrator, which is a robust simulator for the statics of large deformation elasticity, using automatic differentiation as a first class citizen. Building this simulator allows us easy interoperability between machine learning and solid mechanics problems, and has been used as in several published and in submission works. I will then describe Neuromechanical Autoencoders, where we coupled neural network controllers with mechanical metamaterials to create artificial mechanical intelligence. The neural network ”encoder” consumes a representation of the task—in this case, achieving a particular deformation—and nonlinearly transforms this into a set of linear actuations which play the role of the latent encoding. These actuations then displace the boundaries of the mechanical metamaterial inducing another nonlinear transformation due to the complex learned geometry of the pores; the resulting deformation corresponds to the ”decoder”.