Cronograma de las presentaciones
Cronograma de las presentaciones
Link
| Fecha | Responsable | Título | DOI |
| 2024-08-30 | Christian Diaz | PINNs, una presentación gentíl :-) | |
| 2024-09-06 | Christian Diaz | PINNs, una presentación gentíl (Ejemplo y código) | |
| 2024-09-13 | Francisco De Izaguirre | A Physics-informed Deep Neural Network for Harmonization of CT Images | Link |
| 2024-09-20 | Florencia Uslenghi | Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks, | Link |
| 2024-09-27 | Julian Rodriguez | Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations | Link |
| 2024-10-04 | Rafael Rosa | Machine Learning for Physics-Informed Generation of Dispersed Multiphase Flow Using Generative Adversarial Networks | Link |
| 2024-10-11 | Mauricio Ramos | Universal and High-Fidelity Resolution Extending for Fluorescence Microscopy Using a Single-Training Physics-Informed Sparse Neural Network | Link |
| 2024-10-18 | Bruno Jose Zorzet | Physics-informed learning of governing equations from scarce data. |
Link |
| 2024-10-25 | Fatima Alvez Varela | Untrained, physics-informed neural networks for structured illumination microscopy | Link |
| 2024-11-01 | Sofia Zimmer | A geometry-informed deep learning framework for ultra-sparse 3D tomographic image reconstruction | Link |
| 2024-11-08 | Graciana Castro & Mateo Musitelli | TBD | |
| 2024-11-15 | Juan Llaguno | TBD | |
| 2024-11-22 | Camilo Borba | GPINN: Physics-informed Neural Network with Graph Embedding | Link |
| 2024-11-29 | Roman Demczylo | Matriz de Mueller & PINNs | |
| 2024-12-06 | Josefina Catoni | Tema: estimación de parámetros y conectividad funcional a partir de datos de fMRI | Link |
Última modificación: miércoles, 23 de octubre de 2024, 12:15