Sara Garbarino
ODE learning from noisy and scarce data
ODE learning from noisy and scarce data
Learning non-parametric systems of ODE from noisy and sparse data is an emerging machine learning topic. I am currently supervising a PhD project on the use of the adjoint state method and implicit inverse problems to address this challenge.
Discontinuous inverse operators approximation
Discontinuous inverse operators approximation
We have recently presented a regularization method for approximating the solution of parametric inverse problems by leveraging on a dataset of examples of input–output pairs of the forward operator. Our results provide new insights on the use of NNs for the solution of inverse problems.
