Physics-Informed Neural Networks for shell structures

Bastek, Jan-Hendrik; Kochmann, Dennis M.

doi:10.1016/j.euromechsol.2022.104849

Cited by 28 publications

(6 citation statements)

References 39 publications

(60 reference statements)

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“…Bastek and Kochmann [93] employed PINNs to model the small-strain response of shell structures. Zhang et al [46,47] demonstrated that PINNs could effectively identify the inhomogeneous material and geometry distribution under plane-strain conditions.…”

Section: Physics-informed Neural Network (Pinns)mentioning

confidence: 99%

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: Physics-informed Neural Network (Pinns)mentioning

confidence: 99%

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

Jin¹,

Zhang²,

Espinosa³

2023

Preprint

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“…For example, physics‐informed NN models were developed to predict the structural instability, [ 108 ] design the key structural parameters, [ 109,110 ] and optimize the structural response. [ 111–113 ] Interfacial inverse design can be conducted on the contact surfaces of TENGs by physical patterning such as designing various artificially localized morphologies. For example, mechanical metamaterials, manmade structural materials assembled by numerous microstructures in a periodic manner, have recently been applied in TENGs to promote the electrical performance due to their structural programmability.…”

Section: Inverse Design Of Tengs By Physics‐informed Aimentioning

confidence: 99%

Maximizing Triboelectric Nanogenerators by Physics‐Informed AI Inverse Design

Jiao,

Wang,

Alavi

2023

Advanced Materials

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Triboelectric nanogenerators offer an environmentally friendly approach to harvesting energy from mechanical excitations. This capability has made them widely sought‐after as an efficient, renewable, and sustainable energy source, with the potential to decrease reliance on traditional fossil fuels. However, developing triboelectric nanogenerators with specific output remains a challenge mainly due to the uncertainties associated with their complex designs for real‐life applications. Artificial intelligence‐enabled inverse design is a powerful tool to realize performance‐oriented triboelectric nanogenerators. This is an emerging scientific direction that can address the concerns about the design and optimization of triboelectric nanogenerators leading to a next generation nanogenerator systems. This perspective paper aims at reviewing the principal analysis of triboelectricity, summarizing the current challenges of designing and optimizing triboelectric nanogenerators, and highlighting the physics‐informed inverse design strategies to develop triboelectric nanogenerators. Strategic inverse design is particularly discussed in the contexts of expanding the four‐mode analytical models by physics‐informed artificial intelligence, discovering new conductive and dielectric materials, and optimizing contact interfaces. Various potential development levels of artificial intelligence‐enhanced triboelectric nanogenerators are delineated. Finally, the potential of physics‐informed artificial intelligence inverse design to propel triboelectric nanogenerators from prototypes to multifunctional intelligent systems for real‐life applications is discussed.

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“…[34][35][36][37][38][39] Such innovations in structural inverse design with deep learning have enabled various attempts in MM design, such as lattice structures with superior elastic modulus, controllable auxeticity, and the inverse design of MMs exhibiting target stress-strain curves. [40][41][42][43][44][45] However, a deep learning-based inverse design framework for MMs with target NTE and NPR has not yet been proposed.…”

Section: Introductionmentioning

confidence: 99%