DATA-DRIVEN SOLUTIONS AND PARAMETER ESTIMATIONS OF A FAMILY OF HIGHER-ORDER KDV EQUATIONS BASED ON PHYSICS INFORMED NEURAL NETWORKS

Data-driven solutions and parameter estimations of a family of higher-order KdV equations based on physics informed neural networks

Abstract Physics informed neural network (PINN) demonstrates powerful capabilities in solving forward and inverse problems of nonlinear partial differential equations (NLPDEs) through combining data-driven and physical constraints.In this paper, two PINN methods that adopt tanh and sine as activation functions, respectively, are used to study data-

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The Paradigm of Disability

The kt196 torque converter aim of this literature study is to catch up on the inclusive development issues in the conception of disability perspective and participation of persons with disabilities in the development goals.This topic is in line with global development which targets seek to accommodate the needs of every citizen including persons wi

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Prediction of the in-plane permeability and air evacuation time of fiber-placed thermoplastic composite preforms with engineered intertape channels

The two main void removal mechanisms during vacuum-bag-only (VBO) consolidation of thermoplastic composites are through-thickness diffusion and in-plane air evacuation.The automated fiber placement (AFP) process allows for the creation of preforms with an engineered intertape channel network by deliberately introducing spacing here between the tape

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