Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine
learning. Automatic differentiation (AD), also called algorithmic differentiation or simply" auto …

Linear model predictive control (MPC) can be currently deployed at outstanding speeds,
thanks to recent progress in algorithms for solving online the underlying structured quadratic …

We introduce JAX MD, a software package for performing differentiable physics simulations
with a focus on molecular dynamics. JAX MD includes a number of statistical physics …

Abstract The Forward-Backward Sweep Method is a numerical technique for solving optimal
control problems. The technique is one of the indirect methods in which the differential …

Several cardiovascular diseases are caused from localised abnormal blood flow such as in
the case of stenosis or aneurysms. Prevailing theories propose that the development is …

An adjoint formulation for the gradient-based optimization of oil–gas compositional reservoir
simulation problems is presented. The method is implemented within an automatic …

A large fraction of computational science involves simulating the dynamics of particles that
interact via pairwise or many-body interactions. These simulations, called Molecular …

We introduce JAX MD, a software package for performing differentiable physics simulations
with a focus on molecular dynamics. JAX MD includes a number of physics simulation …

A new stellarator coil design code is introduced that optimizes the position and winding pack
orientation of finite-build coils. The new code, called flexible optimized curves in space using …

A central problem in uncertainty quantification is how to characterize the impact that our
incomplete knowledge about models has on the predictions we make from them. This …