The article discusses the use of Physics-Informed Neural Networks (PINNs) for solving partial differential equations (PDEs). However, PINNs can face difficulties when dealing with equations with rapidly changing solutions. To…
Continue readingPhysics-informed neural network (PINN) is a data-driven solver for differential equations, providing a unified framework to address both forward and inverse problems. However, the complexity of the objective function often…
Continue readingThe paper proposes a solution for two-player general-sum differential games using a Pontryagin-mode neural operator. The authors address the curse of dimensionality and convergence issues through physics-informed neural networks. The…
Continue readingThis academic paper presents a new framework for predicting temperature fields in real-time during metal additive manufacturing (AM) processes. The authors argue that accurate temperature prediction is essential to prevent…
Continue readingThis paper presents a new approach to predicting temperature fields in metal additive manufacturing (AM) using a physics-informed neural network framework. Traditional physics-based models are often time-consuming and unsuitable for…
Continue readingThis paper presents an approach to estimate system parameters of electrical submersible pumps (ESPs) used in the oil and gas industry, employing Physics-Informed Neural Networks (PINNs). ESPs often handle multiphase…
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