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Learning Stiffness Tensors in Self-Activated Solids via a Local Rule
Yuxuan Tang1, Wenjing Ye1, Jingjing Jia2
1Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.
Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|March 14, 2024
Summary
This study introduces self-activated solids, a new class of active metamaterials that learn desired stiffness properties from experienced deformations. This enables autonomous adaptation for improved material performance and functionality.
Area of Science:
- Materials Science
- Mechanical Engineering
- Artificial Intelligence
Background:
- Mechanical metamaterials are typically designed for specific functions.
- Existing adaptive materials face challenges in computation and coordination.
Purpose of the Study:
- To develop active lattice metamaterials, termed self-activated solids, capable of learning desired stiffness tensors autonomously.
- To overcome limitations of off-line computations and intricate element coordination in adaptive materials.
Main Methods:
- Introduced a simple online and local learning strategy based on contrastive Hebbian learning.
- Varied bond stiffness based solely on local strain during learning.
- Utilized numerical tests to validate the material's learning capabilities.
Main Results:
- Self-activated solids autonomously achieved desired bulk, shear, and coupling moduli.
- Demonstrated the ability to form uni-mode and bi-mode extremal materials.
- Showcased achievement of time-varying moduli when exposed to different loads.
Conclusions:
- The proposed design is applicable to various lattice geometries and robust against damage.
- This approach benefits the design of autonomous materials, physical learning machines, and adaptive robots.
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