Fleur Hendriks
Papers
2
Total Citations
8
H-Index
2
About
Fleur Hendriks is a rising researcher at the forefront of computational mechanics and materials design, whose work bridges the gap between machine learning and mechanical metamaterials. Her primary research focuses on leveraging symmetry principles—specifically wallpaper group symmetries—to understand and predict the behavior of soft, porous metamaterials. These innovative materials undergo dramatic pattern transformations under load, making them promising for applications in soft robotics, sound reduction, and biomedicine. Hendriks’ major contributions include the development of "Similarity equivariant graph neural networks for homogenization of metamaterials" (2025, 6 citations), a pioneering approach that uses geometric deep learning to simulate complex material behaviors with unprecedented accuracy and speed. She also created the "Wallpaper Group-Based Mechanical Metamaterials: Dataset Including Mechanical Responses" (2025, 2 citations), a foundational dataset that systematically catalogs how symmetry dictates mechanical properties. This work enables researchers to rapidly tune material responses for specific applications. Though early in her career, Hendriks is establishing herself as a key figure in the emerging field of symmetry-aware AI for materials science. Her work promises to accelerate the design of next-generation adaptive materials, making her a researcher to watch closely.
Research Focus
Key Achievements
Top Papers
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