14 citations · 16 across the 4 of their papers we have counts for
4 papers
Neural Networks for Threshold Dynamics Reconstruction
Elisa Negrini, Almanzo Jiahe Gao, Abigail Bowering +2
We introduce two convolutional neural network (CNN) architectures, inspired by the Merriman-Bence-Osher (MBO) algorithm and by cellular automatons, to model and learn threshold dyn…
Applications of ML-Based Surrogates in Bayesian Approaches to Inverse Problems
Pelin Ersin, Emma Hayes, Peter Matthews +3
Neural networks have become a powerful tool as surrogate models to provide numerical solutions for scientific problems with increased computational efficiency. This efficiency can…
A Neural Network Ensemble Approach to System Identification
Elisa Negrini, Giovanna Citti, Luca Capogna
We present a new algorithm for learning unknown governing equations from trajectory data, using and ensemble of neural networks. Given samples of solutions to an unknown dyn…
System Identification Through Lipschitz Regularized Deep Neural Networks
Elisa Negrini, Giovanna Citti, Luca Capogna
In this paper we use neural networks to learn governing equations from data. Specifically we reconstruct the right-hand side of a system of ODEs directly…