A multigradient algorithm using a mixture of experts architecture for land cover classification of multisensor images

Nair, Pramod P

doi:10.1080/01431161.2010.493564

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A Physics Informed Neural Network1

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2015

2023

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Cited by 5 publications

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“…The required derivatives can be calculated using automatic differentiation at the end of the network. It is possible to train a nonlinear neural network using this approach and its layers of hidden nodes [4]. It then finds a loss function corresponding to the differential equation and boundary conditions.…”

Section: A Physics Informed Neural Networkmentioning

confidence: 99%

Physics-informed Neural Networks approach to solve the Blasius function

Greeshma¹,

Nair²,

Nair³

et al. 2023

Preprint

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Deep learning techniques with neural networks have been used effectively in computational fluid dynamics (CFD) to obtain solutions to nonlinear differential equations. This paper presents a physics-informed neural network (PINN) approach to solve the Blasius function. This method eliminates the process of changing the non-linear differential equation to an initial value problem. Also, it tackles the convergence issue arising in the conventional series solution. It is seen that this method produces results that are at par with the numerical and conventional methods. The solution is extended to the negative axis to show that PINNs capture the singularity of the function at η = −5.69.

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Section: A Physics Informed Neural Networkmentioning

confidence: 99%