Search Results for author:
Found 3 papers, 0 papers with code
Enhancing Data-Assimilation in CFD using Graph Neural Networks
We present a novel machine learning approach for data assimilation applied in fluid mechanics, based on adjoint-optimization augmented by Graph Neural Networks (GNNs) models.
Neural State-Dependent Delay Differential Equations
The developed framework is auto-differentiable and runs efficiently on multiple backends.
Curriculum learning for data-driven modeling of dynamical systems
Based on that, we consider entropy as a metric of complexity of the dataset; we show how an informed design of the training set based on the analysis of the entropy significantly improves the resulting models in terms of generalizability, and provide insights on the amount and the choice of data required for an effective data-driven modeling.
