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RISSolving Inverse Heat Transfer Problems Without Surrogate Models: A Fast, Data-Sparse, Physics Informed Neural Network Approach
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Physics informed neural networks have been recently gaining attention for effectively solving a wide variety of partial differential equations. Unlike the traditional machine learning techniques that require experimental ...
Solution of Biharmonic Equation in Complicated Geometries With Physics Informed Extreme Learning Machine
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Recently, physics informed neural networks (PINNs) have produced excellent results in solving a series of linear and nonlinear partial differential equations (PDEs) without using any prior data. However, due to slow training ...
Aerothermal Performance of Axially Varying Winglet-Squealer Blade Tips
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: High-pressure turbine blades are usually susceptible to secondary flow losses due to fluid flow between the casing and the blade tip. In this study, we have evaluated the performance of several blade tip designs for different ...
On Computational Aspects of Least-Squares Projection-Based Model Reduction for Conductive–Radiative Systems
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Fast and accurate reduced order models (ROMs) of conductive–radiative systems are important for several industrial applications, such as spacecraft, radiant furnaces, solar collectors, etc. The nonlinear nature of radiative ...