1 citations · 1 across the 2 of their papers we have counts for
4 papers
NEXT: Physics-Informed Neuro-Spectral Exponential Time Differencing Architectures
Márcio Marques, Leonardo Mendonça, Leonardo M. Moreira +6
Physics-Informed Neural Networks (PINNs) build neural representations of time-dependent PDE solutions, naturally incorporating physics knowledge and observational data, which makes…
A perfectly matched layer approach for the spectral split-step Padé method
Daniel Walsken, Matthias Ehrhardt, Pavel Petrov
The split-step-Padé (SSP) method is widely used to model wave phenomena in various applications, including radio physics, optics and acoustics. In this method, the propagator of th…
Neuro-Spectral Architectures for Causal Physics-Informed Networks
Arthur Bizzi, Leonardo M. Moreira, Márcio Marques +9
Physics-Informed Neural Networks (PINNs) have emerged as a powerful framework for solving partial differential equations (PDEs). However, standard MLP-based PINNs often fail to con…
Square Root Operators and the Well-Posedness of Pseudodifferential Parabolic Models of Wave Phenomena
Matthias Ehrhardt, Jochen Glück, Pavel Petrov +1
Pseudodifferential parabolic equations with an operator square root arise in wave propagation problems as a one-way counterpart of the Helmholtz equation. The expression under the…