98 citations · 114 across the 14 of their papers we have counts for
15 papers
Learning Unsteady Aneurysm Hemodynamics with Physics-Informed DeepONets
Oscar L. Cruz-Gonzalez, Valérie Deplano, Badih Ghattas
Clinically actionable, patient-specific hemodynamic assessment, specifically wall shear stress, vortex structure and pressure distributions, is critical for determining risky or un…
Balanced Twins: Causal Inference on Time Series with Hidden Confounding
Maha Ouali, Badih Ghattas, Emmanuel Flachaire +2
Accurately estimating treatment effects in time series is essential for evaluating interventions in real-world applications, especially when treatment assignment is biased by unobs…
Clustering Approaches for Mixed-Type Data: A Comparative Study
Badih Ghattas, Alvaro Sanchez San-Benito
Clustering is widely used in unsupervised learning to find homogeneous groups of observations within a dataset. However, clustering mixed-type data remains a challenge, as few exis…
Enhanced Vascular Flow Simulations in Aortic Aneurysm via Physics-Informed Neural Networks and Deep Operator Networks
Oscar L. Cruz-González, Valérie Deplano, Badih Ghattas
Due to the limited accuracy of 4D Magnetic Resonance Imaging (MRI) in identifying hemodynamics in cardiovascular diseases, the challenges in obtaining patient-specific flow boundar…
Transfer Learning for Linear Regression: a Statistical Test of Gain
David Obst, Badih Ghattas, Jairo Cugliari +3
Transfer learning, also referred as knowledge transfer, aims at reusing knowledge from a source dataset to a similar target one. While many empirical studies illustrate the benefit…
Textual Data for Time Series Forecasting
David Obst, Badih Ghattas, Sandra Claudel +3
While ubiquitous, textual sources of information such as company reports, social media posts, etc. are hardly included in prediction algorithms for time series, despite the relevan…