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Physics-informed machine learning of the correlation functions in bulk fluids
The Ornstein-Zernike (OZ) equation is the fundamental equation for pair correlation function computations in the modern integral equation theory for liquids.
Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model
To solve the 2D model with the PINN approach, a composite neural network is employed to approximate species concentration and potentials; the input and output are normalized according to prior knowledge of the battery system; the governing equations and boundary conditions are first scaled to an order of magnitude around 1, and then further balanced with a self-weighting method.
Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations
Physics-informed neural networks have emerged as an alternative method for solving partial differential equations.
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