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UKAEA-CCFE-PR(25)3342025
This study aims to systematically examine various methods leveraging machine learning (ML) to assimilate data for investigating thermal systems. These measured or observed data may include temperature or thermal material properties and could be synthetically (computationally) generated or experimentally obtained. The goal of these ML-augmented meth…
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UKAEA-CCFE-PR(25)3182024
In recent years, Physics-Informed Neural Networks (PINNs) have gained popularity, across different engineering disciplines, as an alternative to conventional numerical techniques for solving partial differential equations (PDEs). PINNs are physics-based deep learning frameworks that seamlessly integrate the measurements and the PDE in a multitask l…
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UKAEA-CCFE-PR(23)1872023
Solution reconstruction from limited number of measurements is useful in many areas of heat transfer applications. Unlike the standard problems, such reconstruction problems are ill-posed; thus, the non-uniqueness of solution and inherent instability severely complicates the modelling process. Consequently, more conventional inverse analysis method…
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UKAEA-CCFE-PR(23)1862023
In recent years, physics-informed neural networks (PINN) have been used to solve stiff-PDEs mostly in the 1D and 2D spatial domain. PINNs still experience issues solving 3D problems, especially, problems with conflicting boundary conditions at adjacent edges and corners. These problems have discontinuous solutions at edges and corners that are di…
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