Generating annotations is laborious and time-consuming for human experts, especially in medical image segmentation.
The prevalent methods cannot solve advection dominant problems.
Extracting such a graphical representation of the model's behavior on an abstract, higher conceptual level would unravel the learnings of these models and would help us to evaluate the steps taken by the model for predictions.
However, the size of images and variability in histopathology tasks makes it a challenge to develop an integrated framework for histopathology image analysis.
The predictions made by the TBNN are tested against two different isotropic turbulence datasets at Reynolds number of 433 (JHTD) and 315 (UP Madrid turbulence database, UPMTD) and channel flow dataset at Reynolds number of 1000 (UT Texas and JHTD).
The physics informed neural network (PINN) is evolving as a viable method to solve partial differential equations.
There has been rapid progress recently on the application of deep networks to the solution of partial differential equations, collectively labelled as Physics Informed Neural Networks (PINNs).
In this paper we propose the use of image pixel position coordinate system to improve image classification accuracy in various applications.