Constitutive model characterization and discovery using physics-informed deep learning

Haghighat, Ehsan; Abouali, Sahar; Vaziri, Reza

doi:10.1016/j.engappai.2023.105828

Cited by 32 publications

(3 citation statements)

References 33 publications

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“…To improve the friction materials in TENGs, research efforts have been dedicated to finding the compositions with optimal triboelectric capability. [85] To this end, physic-informed AI inverse design can be a powerful tool to characterize material properties, [86][87][88] obtaining advanced compositions by varying the compositions in their functional chemical groups (e.g., sulfur groups, hydroxyl groups, amine groups, etc. ), material components, and functional fillers.…”

Section: Materials Discovery For Frictionally Conductive Materialsmentioning

confidence: 99%

“…To this end, physic‐informed AI inverse design can be a powerful tool to characterize material properties, [ 86–88 ] obtaining advanced compositions by varying the compositions in their functional chemical groups (e.g., sulfur groups, hydroxyl groups, amine groups, etc. ), material components, and functional fillers.…”

Section: Inverse Design Of Tengs By Physics‐informed Aimentioning

confidence: 99%

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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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Section: Materials Discovery For Frictionally Conductive Materialsmentioning

confidence: 99%

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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“…In the work of (Wu et al 2023), they apply PINN for identifying material properties in linear elasticity. Another important work was done by (Haghighat et al 2023) where they use PINN to characterize constitutive models for elastoplastic solids. (Oommen et al 2022) applied PINN to discover constitutive models in problems involving elastic and viscoplastic solids.…”

Section: Introductionmentioning

confidence: 99%

Physics-informed data based neural networks for two-dimensional turbulence

2022

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Turbulence remains a problem that is yet to be fully understood, with experimental and numerical studies aiming to fully characterise the statistical properties of turbulent flows. Such studies require huge amount of resources to capture, simulate, store and analyse the data. In this work, we present physics-informed neural network (PINN) based methods to predict flow quantities and features of two-dimensional turbulence in a rectangular domain with periodic boundaries. While the PINN model can reproduce all the statistics at large scales, the small scale properties are not captured properly. We introduce a new PINN model that can effectively capture the energy distribution at small scales performing better than the standard PINN based approach. It relies on the training of the low and high wavenumber behaviour separately leading to a better estimate of the full turbulent flow. With 0.1 % training data, we observe that the new PINN model captures the turbulent field at inertial scales leading to a general agreement of the kinetic energy spectra upto eight to nine decades as compared with the solutions from direct numerical simulation (DNS). We further apply these techniques to successfully capture the statistical behaviour of large scale modes in the turbulent flow. We believe such methods to have significant applications in enhancing the retrieval of existing turbulent data sets at even shorter time intervals.

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Data‐Driven Modeling in Geomechanics

KARAPIPERIS

2024

Machine Learning in Geomechanics 2

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Constitutive model characterization and discovery using physics-informed deep learning

Cited by 32 publications

References 33 publications

Maximizing Triboelectric Nanogenerators by Physics‐Informed AI Inverse Design

Maximizing Triboelectric Nanogenerators by Physics‐Informed AI Inverse Design

Physics-informed data based neural networks for two-dimensional turbulence

Data‐Driven Modeling in Geomechanics

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