Scientific machine learning · Inverse problems · Geophysics
Ana Gabriela
Mantilla Dulcey
Geologist · M.Sc. student in Geophysics · Universidad Industrial de Santander
I develop physics-aware machine-learning and optimization methods for inverse problems in computational geophysics. My goal is to turn complex Earth data into reliable, scalable scientific inference.
Universidad Industrial de Santander
2 first-author
Physics-aware learning and optimization
11 with public documentation
Research agenda
Dependable learning for scientific inverse problems
How can we design learning-enabled inverse methods that remain faithful to physics, quantify what the data do and do not support, and scale to the resolution required for scientific discovery?
Selected scholarship
Evidence behind the agenda
First author · journal article · IEEE Transactions on Geoscience and Remote Sensing
Imaging-Guided Pattern Optimization for Seismic Acquisition Geometries Design With Deep Learning
First author · journal article · Gondwana Research
Porphyry-type mineral prospectivity mapping with imbalanced data via prior geological transfer learning
journal article · The Leading Edge
Poststack seismic data denoising via dynamic guided learning
News
Recent recognition
FIRENET places second nationally in the Datos a la U competition
Ana Gabriela Mantilla Dulcey and teammates León Santiago Suárez Rodríguez and Luis Miguel Rodríguez López were recognized for a geospatial AI platform supporting forest-fire management.