Dynamic fracture of a bicontinuously nanostructured copolymer: A deep-learning analysis of big-data-generating experiment

Jin, Hanxun; Jiao, Tifeng; Clifton, R. J.; Kim, Kyung Suk

doi:10.1016/j.jmps.2022.104898

Cited by 13 publications

(8 citation statements)

References 41 publications

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“…Using CNN for feature extraction from interferometric fringes can bypass the need for a conventional fringe unwrapping process while also increasing the feature extraction accuracy. For example, Jin et al [53] employed a CNN-based DL framework to extract dynamic cohesive properties and fracture toughness of polyurea directly from image-shearing interferometric fringes. Kaviani and Kolinski [54] developed a CNNbased DL framework to convert fringes from Fizeau interferometry with low resolution into frustrated total internal reflection (FTIR) images with high resolution while studying droplet impact.…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

“…For its application in Solid Mechanics, cGAN has been employed to inversely identify the material modulus map from the given strain/stress images [45] or predict strain and stress distributions for complex composites [69,70]. Furthermore, cGAN has been successfully applied to experimental data inpainting when partial experimental data is missing [53].…”

Section: Network (Cgans)mentioning

confidence: 99%

“…When predicting dynamic fracture toughness under ultra-high loading rates, the con-ventional experimental measurement of load-displacement is not accessible. To this end, Jin et al [53] proposed an ML-assisted big-data-generating experimental framework that can accurately measure the dynamic fracture toughness and cohesive parameters of samples from plate impact experiments. As shown in Fig.…”

Section: For Fracture Mechanicsmentioning

confidence: 99%

“…The emerging development of nanotechnology and biotechnology in recent several decades continuously requires a new understanding of micro-and nano-scale material behaviors. For example, much research has been conducted on understanding the mechanical properties of thin films [177,178,179,180,181,182,183,184,185], nanos-tructured metals [186,187,188,189,190], sub-micron-sized sensors [191], crystalline nanowires [192,193,194,195,196,197], 2D materials [198,199,200,201,202], origami [203], nanolattice metamaterials [204,205,206], and copolymers with nanoscale features [53,207,208,209]. Conducting precise experiments to characterize these materials with small-scale features is essential to understand their properties, underlining mechanisms, and develop constitutive models.…”

Section: Micro and Nano-mechanicsmentioning

confidence: 99%

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Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review

Jin¹,

Zhang²,

Espinosa³

2023

Preprint

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For many decades, experimental solid mechanics has played a crucial role in characterizing and understanding the mechanical properties of natural and novel materials. Recent advances in machine learning (ML) provide new opportunities for the field, including experimental design, data analysis, uncertainty quantification, and inverse problems. As the number of papers published in recent years in this emerging field is exploding, it is timely to conduct a comprehensive and up-to-date review of recent ML applications in experimental solid mechanics. Here, we first provide an overview of common ML algorithms and terminologies that are pertinent to this review, with emphasis placed on physics-informed and physics-based ML methods. Then, we provide thorough coverage of recent ML applications in traditional and emerging areas of experimental mechanics, including fracture mechanics, biomechanics, nano-and micro-mechanics, architected materials, and 2D material. Finally, we highlight some current challenges of applying ML to multi-modality and multi-fidelity experimental datasets and propose several future research directions. This review aims to provide valuable insights into the use of ML methods as well as a variety of examples for researchers in solid mechanics to integrate into their experiments.

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Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Section: Network (Cgans)mentioning

confidence: 99%

Section: For Fracture Mechanicsmentioning

confidence: 99%

Section: Micro and Nano-mechanicsmentioning

confidence: 99%

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Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review

Jin¹,

Zhang²,

Espinosa³

2023

Preprint

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“…Recently, deep learning algorithms have been employed extensively in data-driven studies of mechanical behavior, ranging from engineering materials to biological tissues. Some studies focus on (constitutive-) model-based approaches, where the deep learning algorithm seeks to identify optimal material parameters in an analytically expressed constitutive model [9][10][11][12][13][14][15][16][17][18]. By utilizing preexisting constitutive models, such approaches have successfully characterized material parameters in many problems.…”

Section: Introductionmentioning

confidence: 99%

G2Φnet: Relating genotype and biomechanical phenotype of tissues with deep learning

Zhang

Spronck²,

Humphrey³

et al. 2022

PLoS Comput Biol

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Many genetic mutations adversely affect the structure and function of load-bearing soft tissues, with clinical sequelae often responsible for disability or death. Parallel advances in genetics and histomechanical characterization provide significant insight into these conditions, but there remains a pressing need to integrate such information. We present a novel genotype-to-biomechanical phenotype neural network (G2Φnet) for characterizing and classifying biomechanical properties of soft tissues, which serve as important functional readouts of tissue health or disease. We illustrate the utility of our approach by inferring the nonlinear, genotype-dependent constitutive behavior of the aorta for four mouse models involving defects or deficiencies in extracellular constituents. We show that G2Φnet can infer the biomechanical response while simultaneously ascribing the associated genotype by utilizing limited, noisy, and unstructured experimental data. More broadly, G2Φnet provides a powerful method and a paradigm shift for correlating genotype and biomechanical phenotype quantitatively, promising a better understanding of their interplay in biological tissues.

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Ultrasound classification of interacting flaws using finite element simulations and convolutional neural network

Niu

Srivastava

2022

Engineering with Computers

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Dynamic fracture of a bicontinuously nanostructured copolymer: A deep-learning analysis of big-data-generating experiment

Cited by 13 publications

References 41 publications

Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review

Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review

G2Φnet: Relating genotype and biomechanical phenotype of tissues with deep learning

Ultrasound classification of interacting flaws using finite element simulations and convolutional neural network

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