Automatic differentiation (autodiff) has revolutionized machine learning. Itallows to express
complex computations by composing elementary ones in creativeways and removes the …

There is an increasing need for effective active learning algorithms that are compatible with
deep neural networks. This paper motivates and revisits a classic, Fisher-based active …

Mechanical metamaterials are artificial structures that exhibit unusual mechanical properties
at the macroscopic level due to architected geometric design at the microscopic level. With …

Chaotic systems are notoriously challenging to predict because of their sensitivity to
perturbations and errors due to time step**. Despite this unpredictable behavior, for many …

Partial differential equations (PDEs) are often computationally challenging to solve, and in
many settings many related PDEs must be be solved either at every timestep or for a variety …

In this paper we show that our Machine Learning (ML) approach, CoMLSim (Composable
Machine Learning Simulator), can simulate PDEs on highly-resolved grids with higher …

We study a one-dimensional model of a dislocation pileup driven by an external stress and
interacting with random quenched disorder, focusing on the predictability of the plastic …

Intelligent biological systems are characterized by their embodiment in a complex
environment and the intimate interplay between their nervous systems and the nonlinear …