Francisco Sahli
email fsahlic@uc.cl
Profile
Francisco Sahli obtuvo su título de Ingeniero Civil Mecánico y Magister en Ciencias de la Ingeniería de la Pontificia Universidad Católica de Chile el año 2011. Al graduarse obtuvo los premios Mario Hiriart, Ismael Valdés Valdés y también el premio de la mejor tesis del departamento de ingeniería mecánica. El año 2014 obtuvo una beca Fulbright-CONICYT para cursar estudios de doctorado en Estados Unidos. El año 2018 obtuvo su doctorado en ingeniería mecánica en Stanford University. Después de una estadía postdoctoral en la Pontificia Universidad Católica de Chile, desde el 2019 se desempeña como profesor asistente en el departamento de Ingeniería Mecánica y Metalúrgica y en el Instituto de Ingeniería Biológica y Médica en la misma casa de estudios.
Network
Keywords from publications
| Title | Year | Doi |
|---|---|---|
| Physics-Informed Neural Networks for Cardiac Activation Mapping | 2020 | https://doi.org/10.3389/fphy.2020.00042 |
| Multi-fidelity classification using Gaussian processes: Accelerating the prediction of large-scale computational models | 2019 | https://doi.org/10.1016/j.cma.2019.112602 |
| The importance of mechano-electrical feedback and inertia in cardiac electromechanics | 2017 | https://doi.org/10.1016/j.cma.2017.03.015 |
| Outbreak dynamics of COVID-19 in Europe and the effect of travel restrictions | 2020 | https://doi.org/10.1080/10255842.2020.1759560 |
| Visualizing the invisible: The effect of asymptomatic transmission on the outbreak dynamics of COVID-19 | 2020 | https://doi.org/10.1016/j.cma.2020.113410 |
| Outbreak dynamics of COVID-19 in China and the United States | 2020 | https://doi.org/10.1007/s10237-020-01332-5 |
| WarpPINN: Cine-MR image registration with physics-informed neural networks | 2023 | https://doi.org/10.1016/j.media.2023.102925 |
| Data-driven anisotropic finite viscoelasticity using neural ordinary differential equations | 2023 | https://doi.org/10.1016/j.cma.2023.116046 |
| Benchmarking physics-informed frameworks for data-driven hyperelasticity | 2023 | https://doi.org/10.1007/s00466-023-02355-2 |
| Machine learning modeling of lung mechanics: Assessing the variability and propagation of uncertainty in respiratory-system compliance and airway resistance | 2024 | https://doi.org/10.1016/j.cmpb.2023.107888 |
| Δ-PINNs: Physics-informed neural networks on complex geometries | 2024 | https://doi.org/10.1016/j.engappai.2023.107324 |
| Learning cardiac activation maps from 12-lead ECG with multi-fidelity Bayesian optimization on manifolds | 2022 | https://doi.org/10.1016/j.ifaco1.2022.09.091 |
| How viscous is the beating heart? Insights from a computational study | 2022 | https://doi.org/10.1007/s00466-022-02180-z |
| Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps | 2022 | https://doi.org/10.1007/s00366-022-01709-3 |
| Data-driven Tissue Mechanics with Polyconvex Neural Ordinary Differential Equations | 2022 | https://doi.org/arXiv:2110.03774 |
| Fast Characterization of Inducible Regions of Atrial Fibrillation Models With Multi-Fidelity Gaussian Process Classification | 2022 | https://doi.org/10.3389/fphys.2022.757159 |
| Sex Differences in Drug-Induced Arrhythmogenesis | 2021 | https://doi.org/10.3389/fphys.2021.708435 |
| COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior | 2021 | https://doi.org/10.1016/j.cma.2021.113891 |
| WarpPINN: Cine-MR image registration with physics-informed neural networks | 2022 | https://doi.org/arXiv:2211.12549 |
| Self-supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representations | 2024 | https://doi.org/10.1007/978-3-031-72104-5_59 |
| Simulation-free prediction of atrial fibrillation inducibility with the fibrotic kernel signature | 2025 | https://doi.org/10.1016/j.media.2024.103375 |
| Assessing Language Lateralization through Gray Matter Volume: Implications for Preoperative Planning in Brain Tumor Surgery | 2024 | https://doi.org/10.3390/brainsci14100954 |
| Understanding the dynamics of the frequency bias in neural networks | 2024 | https://doi.org/arXiv:2405.14957 |
| Generative hyperelasticity with physics-informed probabilistic diffusion fields | 2024 | https://doi.org/10.1007/s00366-024-01984-2 |
School Co-Authors
* Authors who are no longer vigent are not clickable.
External Co-Authors
-
Adrian Buganza Tepole3 publications
-
Vahidullah Tac2 publications
-
Manuel K. Rausch2 publications
-
Leonardo Arrano-Carrasco1 publication
-
Kevin Linka1 publication
-
Sergio Uribe1 publication
-
Hernan Mella1 publication
-
Paris Perdikaris1 publication
-
Sergio Uribe1 publication
-
Carolina Mendez-Orellana1 publication
-
Ellen Kuhl1 publication
-
Pablo Arratia Lopez1 publication
-
Sergio Uribe1 publication
-
Daniel Solomons1 publication
-
Pablo Arratia Lopez1 publication
-
Vahidullah Tac1 publication
-
Jose Barahona1 publication
-
Hernan Mella1 publication
-
Pablo Arratia Lopez1 publication
-
Francisco Mery-Munoz1 publication
-
Hernan Mella1 publication
-
Pablo Arratia Lopez1 publication
-
Ilias Bilionis1 publication
-
Simone Pezzuto1 publication
-
Sergio Uribe1 publication
-
Hernan Mella1 publication