dCG—differentiable connected geometries for AI-compatible multi-domain optimization and inverse design
Luce A, Grünbaum D, Marquardt F (2025)
Publication Language: English
Publication Type: Journal article
Publication year: 2025
Journal
Book Volume: 6
Article Number: 015055
Journal Issue: 1
Abstract
In the domain of geometry and topology optimization, discovering geometries that optimally satisfy specific problem criteria is a complex challenge in both engineering and scientific research. In this work, we propose a new approach for the creation of multidomain connected geometries that are designed to work with automatic differentiation. We introduce the concept of differentiable connected geometries, discussing its theoretical aspects and illustrating its application through simple toy examples and a more sophisticated photonic optimization task. Since these geometries are built upon the principles of automatic differentiation, they are compatible with existing deep learning frameworks, a feature we demonstrate via the application examples. This methodology provides a systematic way to approach geometric design and optimization in computational fields involving dependent geometries, potentially improving the efficiency and effectiveness of optimization tasks in scientific and engineering applications.
Authors with CRIS profile
Alexander Luce
Lehrstuhl für Theoretische Physik
Florian Marquardt
Lehrstuhl für Theoretische Physik
Involved external institutions
Germany (DE)How to cite
APA:
Luce, A., Grünbaum, D., & Marquardt, F. (2025). dCG—differentiable connected geometries for AI-compatible multi-domain optimization and inverse design. Machine Learning: Science and Technology, 6(1). https://doi.org/10.1088/2632-2153/adb3ef
MLA:
Luce, Alexander, Daniel Grünbaum, and Florian Marquardt. "dCG—differentiable connected geometries for AI-compatible multi-domain optimization and inverse design." Machine Learning: Science and Technology 6.1 (2025).
BibTeX: Download