Information Propagation and Encoding in Solids: A Quantitative Approach Towards Mechanical Intelligence
It provides a quantitative framework for evaluating information-processing capabilities in mechanical structures, addressing a foundational gap for the field of mechanical intelligence.
The paper applies information-theoretic tools to quantify how information propagates from applied loads to sensor locations in elastic solids, connecting these measures to mechanical phenomena like Saint-Venant's effect, and demonstrates that geometry and architected materials can tune information transmission.
Engineered systems typically separate mechanical function from information processing, whereas biological systems can exploit physical structure as a medium for information processing and computation. Motivated by this contrast, recent work in mechanics has explored embedding information-processing capabilities directly into mechanical structures. However, quantitative frameworks for evaluating such capabilities remain limited. Here we address a foundational question: how does information propagate through a solid body? Using elastic bodies as a model system, we apply information-theoretic tools to treat an elastic domain as an information encoder and quantify how information transmits from applied loads to discrete sensor locations. We further connect these measures to familiar mechanical phenomena, including Saint-Venant's effect and principal stress lines. Moving toward design, we show how geometry and architected materials can tune transmission, enabling elastic domains to either transmit or block information. Overall, this work advances quantifiable metrics and benchmark tasks for mechanical intelligence, supporting comparable designs of mechanically embodied information processing.