4 papers
A Scoping Review of Physics Informed Machine Learning for Wave Propagation Modeling in Seismology
Ãscar Rincón-Cardeño, Gregorio Pérez-Bernal, Silvana Montoya-Noguera +1
\emph{Background:} Standard numerical methods accurately simulate seismic waves but are computationally expensive, particularly for inverse problems. Machine learning approaches ha…
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…
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…
Decoding street network morphologies and their correlation to travel mode choice
Juan Fernando Riascos-Goyes, Michael Lowry, Nicolás GuarÃn-Zapata +1
Urban morphology has long been recognized as a factor shaping human mobility, yet comparative and formal classifications of urban form across metropolitan areas remain limited. Bui…