Efficient computation of states and sensitivities for compound structural optimisation problems using a Linear Dependency Aware Solver (LDAS)
Stijn Koppen, Max van der Kolk, Sanne van den Boom, Matthijs Langelaar

TL;DR
This paper introduces LDAS, a solver that automatically detects linear dependencies among loads in structural optimisation problems, reducing computational effort by avoiding unnecessary solves.
Contribution
The paper presents a novel Linear Dependency Aware Solver (LDAS) that efficiently detects and exploits load dependencies in structural optimisation, improving computational efficiency.
Findings
LDAS detects linear dependencies automatically.
Using LDAS reduces the number of solves needed.
Demonstrated benefits in illustrative examples and run-time experiments.
Abstract
Real-world structural optimisation problems involve multiple loading conditions and design constraints, with responses typically depending on states of discretised governing equations. Generally, one uses gradient-based nested analysis and design approaches to solve these problems. Herein, solving both physical and adjoint problems dominates the overall computational effort. Although not commonly detected, real-world problems can contain linear dependencies between encountered physical and adjoint loads. Manually keeping track of such dependencies becomes tedious as design problems become increasingly involved. To detect and exploit such dependencies, this work proposes the use of a Linear Dependency Aware Solver (LDAS), which is able to efficiently detect linear dependencies between all loads to avoid unnecessary solves entirely and automatically. Illustrative examples are provided…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Multi-Objective Optimization Algorithms · Probabilistic and Robust Engineering Design · Model Reduction and Neural Networks
