A Computational Framework for the Indirect Estimation of Interface Thermal Resistance of Composite Materials Using Xpinns

Papadopoulos, Leonidas; Bakalakos, Serafeim; Nikolopoulos, Stefanos; Kalogeris, Ioannis; Papadopoulos, Vissarion

doi:10.2139/ssrn.4065418

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Ai and Data Exchange Procedures1

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2023

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Cited by 2 publications

(2 citation statements)

References 25 publications

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“…Physics equations can be used to build the engine of solving complex tasks, i.e. in the calculation of unknown equation parameters via the utilisation of partial differential equations, in efficient manner and is expected to lead to fruitful and productive outcomes [28]. Simplicity provided by any means of AI or assessment methods is appealing and sometimes very powerful.…”

Section: Ai and Data Exchange Proceduresmentioning

confidence: 99%

Materials characterisation and software tools as key enablers in Industry 5.0 and wider acceptance of new methods and products

Konstantopoulos,

Charitidis,

Bañares

et al. 2023

Materials Today Communications

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Section: Ai and Data Exchange Proceduresmentioning

confidence: 99%

Materials characterisation and software tools as key enablers in Industry 5.0 and wider acceptance of new methods and products

Konstantopoulos,

Charitidis,

Bañares

et al. 2023

Materials Today Communications

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“…Moreover, all these methods are valid for high-dimensional equations, avoiding the curse of dimensionality. In recent years, a number of other updated versions of PINN have also been published and successfully utilized in a variety of fields [16][17][18]. Although these methods have the remarkable superiority mentioned above, they all require repeated training for different equations, which is inappropriate for solving the PPE.…”

Section: Introductionmentioning

confidence: 99%

A neural network-based Poisson solver for fluid simulation

Jiang¹,

Wang²,

Yao³

et al. 2023

Preprint

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The pressure Poisson equationis usually the most time-consuming problem in fluid simulation. To accelerate its solving process, we propose a deep neural network-based numerical method, termed Deep Residual Iteration Method (DRIM), in this paper. Firstly, the global equation is decomposed into multiple independent tridiagonal sub-equations, and DRIM is capable of solving all the sub-equations simultaneously. Moreover, we employed Residual Network and a correction iteration method to improve the precisionof the solution achieved by the neural network in DRIM. The numerical results, including the Poiseuille flow, the backwards-facing step flow, and driven cavity flow, have proven that the numerical precision of DRIM is comparable to that of classic solvers. In these numerical cases, the DRIM-based algorithm is about 2 to 10 times faster than the conventional method, which indicates that DRIM has promising applications in large-scale problems.

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A Computational Framework for the Indirect Estimation of Interface Thermal Resistance of Composite Materials Using Xpinns

Cited by 2 publications

References 25 publications

Materials characterisation and software tools as key enablers in Industry 5.0 and wider acceptance of new methods and products

Materials characterisation and software tools as key enablers in Industry 5.0 and wider acceptance of new methods and products

A neural network-based Poisson solver for fluid simulation

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