48 citations · 177 across the 8 of their papers we have counts for
3 papers · 1 filter
Probing optimisation in physics-informed neural networks
Nayara Fonseca, Veronica Guidetti, Will Trojak
A novel comparison is presented of the effect of optimiser choice on the accuracy of physics-informed neural networks (PINNs). To give insight into why some optimisers are better,…
Generalizing similarity in noisy setups: the DIBS phenomenon
Nayara Fonseca, Veronica Guidetti
This work uncovers an interplay among data density, noise, and the generalization ability in similarity learning. We consider Siamese Neural Networks (SNNs), which are the basic fo…
dNNsolve: an efficient NN-based PDE solver
Veronica Guidetti, Francesco Muia, Yvette Welling +1
Neural Networks (NNs) can be used to solve Ordinary and Partial Differential Equations (ODEs and PDEs) by redefining the question as an optimization problem. The objective function…