With the explosive growth of computational resources and data generation, deep machine learning has been successfully employed in various applications. One important and emerging scientific application of deep learning involves solving differential equations. Here, physics-informed neural networks (PINNs) are developed to solve the differential equations associated with a specific scientific problem. As such, algorithms for solving the differential equations by embedding their initial and boundary conditions in the cost function of the artificial neural networks using algorithmic differentiation must also be developed. In this study, various PINNs are adopted to estimate the stresses in the tablets and the interphase of a single lap joint. The proposed model is represented by two fourth-order non-homogeneous coupled partial differential equations, with the axial stresses in the upper and lower tablets adopted as the dependent variables. The axial stresses are a function of the tablet length, which presents the independent variable. Therefore, the axial stresses in the tablets are estimated by solving the coupled partial differential equations when subjected to the boundary conditions, whereas the remaining stress components are expressed in terms of axial stresses. The results obtained using the developed methodology are validated using the results obtained via MAPLE software.

Piezoresistive pressure sensors are widely used in various fields such as in industries, automobiles, medical applications and many others. To get proper input from the sensors we must have a good sensor having good sensitivity in every type of environmental conditions. The behavior of the sensor also depends upon its thickness, area, material properties etc. This paper is based on the piezoresistive micro pressure sensor having greater sensitivity made of silicon material. Using finite element analysis (FEA) and ANSYS the sensitivity is determined on different dimensions with different loading conditions. The piezo resistors of silicon material are arranged in different patterns and the sensitivity of each plate is determined by having all the results best design for the sensor is achieved.

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