3 papers
A Scoping Review of Physics Informed Machine Learning for Wave Propagation Modeling in Seismology
Óscar Rincón-Cardeño, Gregorio Pérez-Bernal, Silvana Montoya-Noguera +1
Standard numerical methods accurately simulate seismic waves but are computationally expensive, particularly for inverse problems. Some machine-learning-based alternatives have eme…
Physics-informed neural networks to solve inverse problems in unbounded domains
Gregorio Pérez-Bernal, Oscar Rincón-Cardeño, Silvana Montoya-Noguera +1
Inverse problems are extensively studied in applied mathematics, with applications ranging from acoustic tomography for medical diagnosis to geophysical exploration. Physics inform…
Benchmarking Physics-Informed Neural Networks and Boundary Elements Methods for Wave Scattering
Oscar Rincón-Cardeno, Gregorio Pérez Bernal, Silvana Montoya Noguera +1
This study compares the Boundary Element Method (BEM) and Physics-Informed Neural Networks (PINNs) for solving the two-dimensional Helmholtz equation in wave scattering problems. T…