Material Fingerprinting for ultra-fast material model discovery without solving optimization problems
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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 and ill-posed 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. We validate the method using numerical and experimental supervised data, including stress-strain data pairs, as well as numerical and experimental unsupervised data, consisting of global force and local displacement measurements.
