This work explores the strategy of adding physical coefficients as predictor variables in a Physics-Informed Neural Network (PINN) for the time-dependent Maxwell linear equations in case electric and magnetic fields present highly oscillatory behavior
Electromagnetic algorithm is a population based meta-heuristic which imitates the attraction and rep...
In this thesis, we focus on developing neural networks algorithms for scientific computing. First, w...
Effective analysis of nonlinear electromagnetic fields is essential for the accurate modeling of ele...
Abstract Electromagnetics (EM) can be described, together with the constitutive laws, by four PDEs, ...
In this work we investigate neural networks and subsequently physics-informed neural networks. Physi...
Electromagnetics (EM) can be described, together with the constitutive laws, by four PDEs, called Ma...
The application of artificial neural networks (ANN) is considered to the problems of electromagnetic...
Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like ...
Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like ...
In deep learning, neural networks consisting of trainable parameters are designed to model unknown f...
The physical world around us is profoundly complex and for centuries we have sought to develop a dee...
In this paper, the recent neural network (NN) approaches to time domain electromagnetic (EM)-based m...
Maxwell's equations govern light propagation and its interaction with matter. Therefore, the solutio...
This paper investigates the application of Physics-Informed Neural Networks (PINNs) in modelling con...
The problem of electro-thermal coupling is widely present in the integrated circuit (IC). The accura...
Electromagnetic algorithm is a population based meta-heuristic which imitates the attraction and rep...
In this thesis, we focus on developing neural networks algorithms for scientific computing. First, w...
Effective analysis of nonlinear electromagnetic fields is essential for the accurate modeling of ele...
Abstract Electromagnetics (EM) can be described, together with the constitutive laws, by four PDEs, ...
In this work we investigate neural networks and subsequently physics-informed neural networks. Physi...
Electromagnetics (EM) can be described, together with the constitutive laws, by four PDEs, called Ma...
The application of artificial neural networks (ANN) is considered to the problems of electromagnetic...
Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like ...
Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like ...
In deep learning, neural networks consisting of trainable parameters are designed to model unknown f...
The physical world around us is profoundly complex and for centuries we have sought to develop a dee...
In this paper, the recent neural network (NN) approaches to time domain electromagnetic (EM)-based m...
Maxwell's equations govern light propagation and its interaction with matter. Therefore, the solutio...
This paper investigates the application of Physics-Informed Neural Networks (PINNs) in modelling con...
The problem of electro-thermal coupling is widely present in the integrated circuit (IC). The accura...
Electromagnetic algorithm is a population based meta-heuristic which imitates the attraction and rep...
In this thesis, we focus on developing neural networks algorithms for scientific computing. First, w...
Effective analysis of nonlinear electromagnetic fields is essential for the accurate modeling of ele...
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