Add to ORKGThis study proposes the physics-informed neural network (PINN) framework to solve the wave equation for acoustic resonance analysis. ResoNet, the analytical model proposed in this study, minimizes the loss function for periodic solutions, in addition to conventional PINN loss functions, thereby effectively using the function approximation capability of neural networks, while performing resonance analysis. Additionally, it can be easily applied to inverse problems. Herein, the resonance in a one-dimensional acoustic tube was analyzed. The effectiveness of the proposed method was validated through the forward and inverse analyses of the wave equation with energy-loss terms. In the forward analysis, the applicability of PINN to the resonance p...
Physics-informed neural networks (PINNs) are revolutionizing science and engineering practice by bri...
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 this thesis we have analysed the behaviour of a physics informed neural network and it’s competen...
Realistic sound is essential in virtual environments, such as computer games and mixed reality. Effi...
Applications of neural network algorithms in rock physics have developed rapidly developed, mainly d...
The Helmholtz equation has been used for modeling the sound pressure field under a harmonic load. Co...
The Vlasov-Poisson system is employed in its reduced form version (1D1V) as a test bed for the appli...
We propose a new approach to the solution of the wave propagation and full waveform inversions (FWIs...
The solution of nonlinear partial differential equations using numerical methods is a difficult proc...
Solving for the frequency-domain scattered wavefield via physics-informed neural network (PINN) has ...
Solving for the frequency-domain scattered wavefield via physics-informed neural network (PINN) has ...
Wavefield reconstruction inversion (WRI) formulates a PDE-constrained optimization problem to reduce...
Physics-Informed Neural Networks (PINNs) are hybrid models that incorporate differential equations i...
An established model for sound energy decay functions (EDFs) is the superposition of multiple expone...
Physics-informed neural networks (PINNs) are revolutionizing science and engineering practice by bri...
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 this thesis we have analysed the behaviour of a physics informed neural network and it’s competen...
Realistic sound is essential in virtual environments, such as computer games and mixed reality. Effi...
Applications of neural network algorithms in rock physics have developed rapidly developed, mainly d...
The Helmholtz equation has been used for modeling the sound pressure field under a harmonic load. Co...
The Vlasov-Poisson system is employed in its reduced form version (1D1V) as a test bed for the appli...
We propose a new approach to the solution of the wave propagation and full waveform inversions (FWIs...
The solution of nonlinear partial differential equations using numerical methods is a difficult proc...
Solving for the frequency-domain scattered wavefield via physics-informed neural network (PINN) has ...
Solving for the frequency-domain scattered wavefield via physics-informed neural network (PINN) has ...
Wavefield reconstruction inversion (WRI) formulates a PDE-constrained optimization problem to reduce...
Physics-Informed Neural Networks (PINNs) are hybrid models that incorporate differential equations i...
An established model for sound energy decay functions (EDFs) is the superposition of multiple expone...
Physics-informed neural networks (PINNs) are revolutionizing science and engineering practice by bri...
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 ...