A scalable matrix-free spectral element approach for unsteady PDE constrained optimization using PETSc/TAO

TL;DR

This paper introduces a scalable, matrix-free spectral element method for unsteady PDE-constrained optimization, leveraging PETSc/TAO to efficiently compute adjoint derivatives without significant additional computational cost.

Contribution

It presents a novel matrix-free approach for the transpose Jacobian application in spectral element methods, enabling efficient PDE-constrained optimization with explicit integrators.

Findings

Efficient adjoint computation reduces optimization cost.

Applicable to large-scale, time-dependent PDE problems.

Seamless integration with PETSc/TAO enhances scalability.

Abstract

We provide a new approach for the efficient matrix-free application of the transpose of the Jacobian for the spectral element method for the adjoint based solution of partial differential equation (PDE) constrained optimization. This results in optimizations of nonlinear PDEs using explicit integrators where the integration of the adjoint problem is not more expensive than the forward simulation. Solving PDE constrained optimization problems entails combining expertise from multiple areas, including simulation, computation of derivatives, and optimization. The Portable, Extensible Toolkit for Scientific computation (PETSc) together with its companion package, the Toolkit for Advanced Optimization (TAO), is an integrated numerical software library that contains an algorithmic/software stack for solving linear systems, nonlinear systems, ordinary differential equations, differential algebraic equations, and large-scale optimization problems and, as such, is an ideal tool for performing PDE-constrained optimization. This paper describes an efficient approach in which the software stack provided by PETSc/TAO can be used for large-scale nonlinear time-dependent problems. While time integration can involve a range of high-order methods, both implicit and explicit. The PDE-constrained optimization algorithm used is gradient-based and seamlessly integrated with the simulation of the physical problem.