A Neural Network Transformer Model for Composite Microstructure Homogenization

no code implementations • 16 Apr 2023 • Emil Pitz, Kishore Pochiraju

This paper illustrates a transformer neural network architecture that captures the knowledge of various microstructures and constituents, enabling it to function as a computationally efficient homogenization surrogate model.

Dimensionality Reduction

Fast Design Space Exploration of Nonlinear Systems: Part I

no code implementations • 5 Apr 2021 • Sanjai Narain, Emily Mak, Dana Chee, Brendan Englot, Kishore Pochiraju, Niraj K. Jha, Karthik Narayan

This paper presents a new method of solving the inverse design problem namely, given requirements or constraints on output, find an input that also optimizes an objective function.

Active Learning Bayesian Optimization +1

Robot Design With Neural Networks, MILP Solvers and Active Learning

no code implementations • 19 Oct 2020 • Sanjai Narain, Emily Mak, Dana Chee, Todd Huster, Jeremy Cohen, Kishore Pochiraju, Brendan Englot, Niraj K. Jha, Karthik Narayan

Central to the design of many robot systems and their controllers is solving a constrained blackbox optimization problem.

Acrobot Active Learning