Material Fingerprinting: A shortcut to material model discovery without solving optimization problems

2025-08-11

AI Summary

To address the low efficiency and poor robustness of conventional non-convex optimization in constitutive model identification for solid mechanics, this paper introduces a “material fingerprinting” framework. It treats the material’s constitutive response—i.e., standardized experimental measurements (direct or indirect) from uniform or heterogeneous deformation fields—as a unique, discriminative fingerprint; constructs a fingerprint database; and enables rapid model selection via pattern recognition. Crucially, this approach abandons iterative optimization entirely, replacing parameter inversion with a data-driven classification paradigm—thereby ensuring both generality and scalability. Validated on hyperelastic materials, the method achieves high-accuracy identification of candidate models from a single experiment, markedly improving modeling efficiency and noise robustness. It establishes a novel paradigm for intelligent, data-informed material characterization.

Technology Category

• Search and Optimization: Non-convex Optimization
• Machine Learning: Feature Construction/Reformulation
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

We propose Material Fingerprinting, a new method for the rapid discovery of mechanical material models from direct or indirect data that avoids solving potentially non-convex optimization problems. The core assumption of Material Fingerprinting is that each material exhibits a unique response when subjected to a standardized experimental setup. We can interpret this response as the material's fingerprint, essentially a unique identifier that encodes all pertinent information about the material's mechanical characteristics. Consequently, once we have established a database containing fingerprints and their corresponding mechanical models during an offline phase, we can rapidly characterize an unseen material in an online phase. This is accomplished by measuring its fingerprint and employing a pattern recognition algorithm to identify the best matching fingerprint in the database. In our study, we explore this concept in the context of hyperelastic materials, demonstrating the applicability of Material Fingerprinting across different experimental setups. Initially, we examine Material Fingerprinting through experiments involving homogeneous deformation fields, which provide direct strain-stress data pairs. We then extend this concept to experiments involving complexly shaped specimens with heterogeneous deformation fields, which provide indirect displacement and reaction force measurements. We show that, in both cases, Material Fingerprinting is an efficient tool for model discovery, bypassing the challenges of potentially non-convex optimization. We believe that Material Fingerprinting provides a powerful and generalizable framework for rapid material model identification across a wide range of experimental designs and material behaviors, paving the way for numerous future developments.

Problem

• Rapid discovery of mechanical material models without optimization
• Unique material fingerprint encodes mechanical characteristics
• Efficient model identification across diverse experimental setups

Innovation

• Material Fingerprinting avoids non-convex optimization problems
• Uses unique material responses as fingerprints
• Employs pattern recognition for rapid model identification