Information Propagation and Encoding in Solids: A Quantitative Approach Towards Mechanical Intelligence

2026-01-28

AI Summary

This study addresses the absence of a quantitative framework for characterizing information propagation and processing capabilities in solid structures, which has hindered the unified design of mechanical functionality and information handling. For the first time, elastic solids are conceptualized as information encoders, integrating information theory with continuum mechanics to establish a quantitative metric for information transfer from external loads to discrete sensors. The work elucidates how geometry and architected materials govern information transmission pathways and efficiency. By linking classical mechanical phenomena—such as Saint-Venant’s principle and principal stress trajectories—to information-theoretic constructs, the authors propose benchmark tasks and evaluation metrics for mechanical intelligence, enabling the active design of information on/off states. This approach provides a foundational theoretical framework and a novel design paradigm for intelligent structural materials.

Technology Category

• Machine Learning: Information Theory
• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• information propagation
• mechanical intelligence
• elastic solids
• information encoding
• quantitative framework

Innovation

• mechanical intelligence
• information propagation
• elastic encoding
• architected materials
• information theory