4 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…
Evaluation of Sampling Strategies and Physics-Informed Kolmogorov--Arnold Networks in Unbounded Domains
Gregorio Pérez-Bernal, Oscar Rincón-Cardeño, Silvana Montoya-Noguera +1
Physics-informed neural networks (PINNs) have emerged as an effective approach for solving partial differential equations (PDEs) by incorporating physical laws into the learning pr…
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…
Decoding street network morphologies and their correlation to travel mode choice
Juan Fernando Riascos-Goyes, Michael Lowry, Nicolás Guarín-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…