A framework for automated PDE-constrained optimisation
arXiv:1302.3894
Abstract
A generic framework for the solution of PDE-constrained optimisation problems based on the FEniCS system is presented. Its main features are an intuitive mathematical interface, a high degree of automation, and an efficient implementation of the generated adjoint model. The framework is based upon the extension of a domain-specific language for variational problems to cleanly express complex optimisation problems in a compact, high-level syntax. For example, optimisation problems constrained by the time-dependent Navier-Stokes equations can be written in tens of lines of code. Based on this high-level representation, the framework derives the associated adjoint equations in the same domain-specific language, and uses the FEniCS code generation technology to emit parallel optimised low-level C++ code for the solution of the forward and adjoint systems. The functional and gradient information so computed is then passed to the optimisation algorithm to update the parameter values. This approach works both for steady-state as well as transient, and for linear as well as nonlinear governing PDEs and a wide range of functionals and control parameters. We demonstrate the applicability and efficiency of this approach on classical textbook optimisation problems and advanced examples.
References in corpus (2)
Cited by in corpus (11)
- Physics Constrained Learning for Data-driven Inverse Modeling from Sparse Observations
- Automatic shape derivatives for transient PDEs in FEniCS and Firedrake
- Tidal turbine array optimisation using the adjoint approach
- Sample Average Approximations of Strongly Convex Stochastic Programs in Hilbert Spaces
- An iteration count estimate for a mesh-dependent steepest descent method based on finite elements and Riesz inner product representation
- High-dimensional Nonlinear Bayesian Inference of Poroelastic Fields from Pressure Data
- Fast PDE-constrained optimization via self-supervised operator learning
- Parameter identification for a damage model using a physics informed neural network
- Automatic calibration of damping layers in finite element time domain simulations
- trlib: A vector-free implementation of the GLTR method for iterative solution of the trust region problem
- FEniCS Application to a Finite Element Quasi-Geostrophic Model. Linear and non-linear analysis of Munk-like Solutions and Data Assimilation Implementation Through Adjoint Method