8 citations · 20 across the 24 of their papers we have counts for
7 papers · 1 filter
From Spectral Methods to Sample Complexity Bounds for Fourier Neural Operators
Nisha Chandramoorthy, Daniel Sanz-Alonso, Nathan Waniorek
We establish approximation and learning guarantees for Fourier neural operators (FNOs) applied to time- solution operators of dissipative evolution equations. The analysis build…
Auto-differentiable data assimilation: Co-learning of states, dynamics, and filtering algorithms
Melissa Adrian, Daniel Sanz-Alonso, Rebecca Willett
Data assimilation algorithms estimate the state of a dynamical system from partial observations, where the successful performance of these algorithms hinges on costly parameter tun…
Bayesian Optimization on Networks
Wenwen Li, Daniel Sanz-Alonso, Ruiyi Yang
This paper studies optimization on networks modeled as metric graphs. Motivated by applications where the objective function is expensive to evaluate or only available as a black b…
Machine Learning for Inverse Problems and Data Assimilation
Eviatar Bach, Ricardo Baptista, Daniel Sanz-Alonso +1
The aim of this book is to demonstrate the potential for ideas in machine learning to impact on the fields of inverse problems and data assimilation. The perspective is one that is…
Auto-differentiable Ensemble Kalman Filters
Yuming Chen, Daniel Sanz-Alonso, Rebecca Willett
Data assimilation is concerned with sequentially estimating a temporally-evolving state. This task, which arises in a wide range of scientific and engineering applications, is part…
Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis
Nicolas Garcia Trillos, Daniel Sanz-Alonso, Ruiyi Yang
Several data analysis techniques employ similarity relationships between data points to uncover the intrinsic dimension and geometric structure of the underlying data-generating me…