Musculoskeletal simulations are used in many different applications, ranging from the
design of wearable robots that interact with humans to the analysis of patients with impaired …

Automatic differentiation (AD) is conventionally understood as a family of distinct algorithms,
rooted in two “modes”—forward and reverse—which are typically presented (and …

One of the major challenges of shape optimisation in practical industrial cases is to
generically parametrise the wide range of complex shapes. A novel approach is presented …

In the framework of fusion energy research, divertor design and model calibration based on
plasma edge codes currently rely either on manual iterative tuning of parameters, or …

We present capabilities and implementation details of the open-source hybrid-parallel
discrete-adjoint flow solver STAMPS (Source Transformed Adjoint Multi-Physics Solver). The …

We address the design of optimal control strategies for high-dimensional stochastic
dynamical systems. Such systems may be deterministic nonlinear systems evolving from …

A general-purpose C++ software program called CGPOPS is described for solving multiple-
phase optimal control problems using adaptive direct orthogonal collocation methods. The …

The six-degree-of-freedom (6-DoF) powered landing guidance problem usually is difficult to
solve for several reasons. First, this problem coupled with low-frequency translational and …

Rapidly evolving computational techniques are making a large gap between scientific
aspiration and code implementation in climate modeling. In this work, we design a simple …

Automatic differentiation (AD) is a technique for computing the derivative of function F: R n→
R m defined by a computer program. Modern applications of AD, such as machine learning …