Add to ORKGThe Vlasov-Poisson system is employed in its reduced form version (1D1V) as a test bed for the applicability of Physics Informed Neural Network (PINN) to the wave-particle resonance. Two examples are explored: the Landau damping and the bump-on-tail instability. PINN is first tested as a compression method for the solution of the Vlasov-Poisson system and compared to the standard neural networks. Second, the application of PINN to solving the Vlasov-Poisson system is also presented with the special emphasis on the integral part, which motivates the implementation of a PINN variant, called Integrable PINN (I-PINN), based on the automatic-differentiation to solve the partial differential equation and on the automatic-integration to solve the ...
The main goal of this thesis was to investigate the methodology of Physics Informed Neural Networks ...
The physical world around us is profoundly complex and for centuries we have sought to develop a dee...
Nuclear fusion is one of the most promising sources of clean and sustainable energy, but it still re...
This work deals with the modeling of plasmas, which are charged-particle fluids. Thanks to machine l...
Kinetic approaches are generally accurate in dealing with microscale plasma physics problems but are...
We explore the use of Physics-Informed Neural Networks (PINNs) for reconstructing full magnetohydrod...
Physics-Informed Neural Networks (PINNs) are hybrid models that incorporate differential equations i...
This study proposes the physics-informed neural network (PINN) framework to solve the wave equation ...
Within integrated tokamak plasma modeling, turbulent transport codes are typically the computational...
We present an ultrafast neural network model, QLKNN, which predicts core tokamak transport heat and ...
17 pages, 11 figures, Physics of Plasmas, ICDDPS 2019 conference paperWe present an ultrafast neural...
A solver for Poisson\u27s equation was developed using the Radix-2 FFT method first invented by Carl...
Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like ...
Abstract: An accurate impact parameter determination in a heavy ion collision is crucial for almost ...
Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like ...
The main goal of this thesis was to investigate the methodology of Physics Informed Neural Networks ...
The physical world around us is profoundly complex and for centuries we have sought to develop a dee...
Nuclear fusion is one of the most promising sources of clean and sustainable energy, but it still re...
This work deals with the modeling of plasmas, which are charged-particle fluids. Thanks to machine l...
Kinetic approaches are generally accurate in dealing with microscale plasma physics problems but are...
We explore the use of Physics-Informed Neural Networks (PINNs) for reconstructing full magnetohydrod...
Physics-Informed Neural Networks (PINNs) are hybrid models that incorporate differential equations i...
This study proposes the physics-informed neural network (PINN) framework to solve the wave equation ...
Within integrated tokamak plasma modeling, turbulent transport codes are typically the computational...
We present an ultrafast neural network model, QLKNN, which predicts core tokamak transport heat and ...
17 pages, 11 figures, Physics of Plasmas, ICDDPS 2019 conference paperWe present an ultrafast neural...
A solver for Poisson\u27s equation was developed using the Radix-2 FFT method first invented by Carl...
Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like ...
Abstract: An accurate impact parameter determination in a heavy ion collision is crucial for almost ...
Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like ...
The main goal of this thesis was to investigate the methodology of Physics Informed Neural Networks ...
The physical world around us is profoundly complex and for centuries we have sought to develop a dee...
Nuclear fusion is one of the most promising sources of clean and sustainable energy, but it still re...