A deep learning approach to the inverse problem of modulus identification in elasticity

Ni, Bo; Gao, Huajian

doi:10.1557/s43577-020-00006-y

Cited by 45 publications

(20 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…cGAN has been applied to image-to-image transitions like image inpainting [67] or image semantic segmentation [68]. 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%

“…Several comprehensive reviews [34,35,36,37,38] have thoroughly surveyed the potential of ML in materials science. In Solid Mechanics, ML has been successfully employed in a wide range of applications, such as constructing surrogate models for constitutive modeling [39,40], advancing multiscale modeling [41,42], designing architected materials [43], extracting unknown mechanical parameters [44], or obtaining the internal material information from externally measured fields [45,46,47]. In these applications, most ML frameworks were trained on synthetic data from computational methods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Jin¹,

Zhang²,

Espinosa³

2023

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Network (Cgans)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Jin¹,

Zhang²,

Espinosa³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…For example, the use of displacement measurements for capturing SHM causalities in various structural geometries was reported in [99][100][101]. Of note, the specific applications using displacement fields to reconstruct elastic and elasto-plastic properties (and corresponding damage characteristics) have been the source of significant research [102][103][104][105][106][107][108][109][110][111][112][113][114][115][116][117]. In the pervasive case where displacement/strain measurements are discretely measured from strain gauges/fibre-optics, inverse methodologies have also been fruitfully employed for damage characterization, pressure and strain mapping, and shape sensing [118][119][120][121][122][123][124][125][126][127][128][129].…”

Section: (B) Static Inverse Problems In Shmmentioning

confidence: 99%

Structural engineering from an inverse problems perspective

et al. 2022

View full text Add to dashboard Cite

The field of structural engineering is vast, spanning areas from the design of new infrastructure to the assessment of existing infrastructure. From the onset, traditional entry-level university courses teach students to analyse structural responses given data including external forces, geometry, member sizes, restraint, etc.—characterizing a forward problem (structural causalities → structural response). Shortly thereafter, junior engineers are introduced to structural design where they aim to, for example, select an appropriate structural form for members based on design criteria, which is the inverse of what they previously learned. Similar inverse realizations also hold true in structural health monitoring and a number of structural engineering sub-fields (response → structural causalities). In this light, we aim to demonstrate that many structural engineering sub-fields may be fundamentally or partially viewed as inverse problems and thus benefit via the rich and established methodologies from the inverse problems community. To this end, we conclude that the future of inverse problems in structural engineering is inexorably linked to engineering education and machine learning developments.

show abstract

“…Furthermore, more recent studies have demonstrated ML’s exceptional capability in solving inverse design problems. That is, through the ML model such as Generative Adversarial Network (GAN) [ 40 , 41 , 42 ] and Conditional Variational Autoencoder (CVAE) [ 43 , 44 , 45 ], chemical molecular or composites structure can be obtained using the desired requirement as the input of ML model.…”

Section: Introductionmentioning

confidence: 99%

Designing Bioinspired Composite Structures via Genetic Algorithm and Conditional Variational Autoencoder

2023

View full text Add to dashboard Cite

Designing composite materials with tailored stiffness and toughness is challenging due to the massive number of possible material and geometry combinations. Although various studies have applied machine learning techniques and optimization methods to tackle this problem, we still lack a complete understanding of the material effects at different positions and a systematic experimental procedure to validate the results. Here we study a two-dimensional (2D) binary composite system with an edge crack and grid-like structure using a Genetic Algorithm (GA) and Conditional Variational Autoencoder (CVAE), which can design a composite with desired stiffness and toughness. The fitness of each design is evaluated using the negative mean square error of their predicted stiffness and toughness and the target values. We use finite element simulations to generate a machine-learning dataset and perform tensile tests on 3D-printed specimens to validate our results. We show that adding soft material behind the crack tip instead of ahead of the tip tremendously increases the overall toughness of the composite. We also show that while GA generates composite designs with slightly better accuracy (both methods perform well, with errors below 20%), CVAE takes considerably less time (~1/7500) to generate designs. Our findings may provide insights into the effect of adding soft material at different locations of a composite system and may also provide guidelines for conducting experiments and Explainable Artificial Intelligence (XAI) to validate the results.

show abstract

A deep learning approach to the inverse problem of modulus identification in elasticity

Cited by 45 publications

References 22 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

Structural engineering from an inverse problems perspective

Designing Bioinspired Composite Structures via Genetic Algorithm and Conditional Variational Autoencoder

Contact Info

Product

Resources

About