Data and figures for reconstruction and multi-scale transfer learning of complex fracture networks
Abstract
The cvPINN-TL-DFN framework introduces a transfer learning approach that bridges lab experiments and field-scale models. It leverages a multi-fidelity loss to adapt features from lab-trained cvPINNs to subsurface discrete fracture networks (DFNs) at the kilometer scale, ensuring consistency in toughness regimes and the preservation of critical fracture connectivity for geothermal applications.
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 4 January 2026
- Language
- EN
- Document type
- Dataset