Section: Overall Objectives

Overall Objectives

Sciport is a temporary sequel to the former project Tropics. The team studies Automatic Differentiation (AD) of algorithms and programs. We work at the junction of two research domains :

• AD theory: We study software engineering techniques, to analyze and transform programs mechanically. Automatic Differentiation (AD) transforms a program P that computes a function $F$, into a program P' that computes analytical derivatives of $F$. We put emphasis on the so-called reverse or adjoint mode of AD, a sophisticated transformation that yields gradients for optimization at a remarkably low cost.

• AD application to Scientific Computing: We apply the adjoint mode of AD to e.g. Computational Fluid Dynamics. We adapt the strategies of Scientific Computing to take full advantage of AD. We validate our work on real-size applications.

Each aspect of our work benefit to the other. We want to produce AD code that can compete with hand-written sensitivity and adjoint programs used in the industry. We implement our algorithms into our tool tapenade , which is one of the most popular AD tools.

Our research directions are :

• Modern numerical methods for finite elements or finite differences : multigrid methods, mesh adaptation.

• Optimal shape design or optimal control in fluid dynamics for steady and unsteady simulations. Higher-order derivatives needed by robust optimization.

• Automatic Differentiation : AD-specific static data-flow analysis, strategies to reduce runtime and memory consumption of the adjoint mode for very large codes. Improved models for adjoint-mode AD, in particular coping with Message-Passing parallellism.