Serialization and deserialization play a dominant role in the state transfer time of serverless workflows, leading to substantial performance penalties during workflow execution. We identify the key reason as a lack o...
Radial Basis Function-generated Finite Differences (RBF-FD) is a meshless method that can be used to numerically solve partial differential equations. The solution procedure consists of two steps. First, the different...
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With the immense computing power at our disposal, the numerical solution of partial differential equations (PDEs) is becoming a day-to-day task for modern computational scientists. However, the complexity of real-life...
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Coastal and Regional Ocean Community model (CROCO) is a modeling system used in oceanographic simulations. CROCO solves primitive hydrodynamic equations for momentum, heat and mass transport on a three dimensional, te...
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One of the most popular methods employed in computational electromagnetics is the Finite Difference Time Domain (FDTD) method. We introduce its generalisation to a meshless setting using the Radial Basis Function gene...
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In this paper, we address a way to reduce the total computational cost of meshless approximation by reducing the required stencil size through spatially varying computational node regularity. Rather than covering the ...
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The numerical stability of fluid flow is an important topic in computational fluid dynamics as fluid flow simulations usually become numerically unstable in the turbulent regime. Many mesh-based methods have already e...
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This paper deals with a numerical analysis of plastic deformation under various conditions, utilizing Radial Basis Function (RBF) approximation. The focus is on the elasto-plastic von Mises problem under plane-strain ...
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When solving partial differential equations on scattered nodes using the Radial Basis Function generated Finite Difference (RBF-FD) method, one of the parameters that must be chosen is the stencil size. Focusing on Po...
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In this study,we present for the first time the application of physics-informed neural network(PINN)to fretting fatigue *** PINN has recently been applied to pure fatigue lifetime prediction,it has not yet been explor...
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In this study,we present for the first time the application of physics-informed neural network(PINN)to fretting fatigue *** PINN has recently been applied to pure fatigue lifetime prediction,it has not yet been explored in the case of fretting *** propose a data-assisted PINN(DA-PINN)for predicting fretting fatigue crack initiation *** traditional PINN that solves partial differential equations for specific problems,DA-PINN combines experimental or numerical data with physics equations as part of the loss function to enhance prediction *** DA-PINN method,employed in this study,consists of two main ***,damage parameters are obtained from the finite element method by using critical plane method,which generates a data set used to train an artificial neural network(ANN)for predicting damage parameters in other ***,the predicted damage parameters are combined with the experimental parameters to form the input data set for the DA-PINN models,which predict fretting fatigue *** results demonstrate that DA-PINN outperforms ANN in terms of prediction accuracy and eliminates the need for high computational costs once the damage parameter data set is ***,the choice of loss-function methods in DA-PINN models plays a crucial role in determining its performance.
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