A reduced order with data assimilation model: Theory and practice
Numerical simulations are extensively used as a predictive tool to better understand
complex air flows and pollution transport on the scale of individual buildings, city blocks and …
complex air flows and pollution transport on the scale of individual buildings, city blocks and …
Optimal reduced space for variational data assimilation
Data Assimilation (DA) is an uncertainty quantification technique used to incorporate
observed data into a prediction model in order to improve numerical forecasted results …
observed data into a prediction model in order to improve numerical forecasted results …
a scalable space-time domain decomposition approach for solving large scale nonlinear regularized inverse ill posed problems in 4D variational data assimilation
L D'Amore, E Constantinescu… - Journal of Scientific …, 2022 - Springer
We address the development of innovative algorithms designed to solve the strong-
constraint Four Dimensional Variational Data Assimilation (4DVar DA) problems in large …
constraint Four Dimensional Variational Data Assimilation (4DVar DA) problems in large …
[HTML][HTML] Parallel implementation of a data assimilation scheme for operational oceanography: The case of the MedBFM model system
The MedBFM model system provides forecasts and reanalysis of the Mediterranean Sea
biogeochemistry for the European Copernicus Services. The system integrates model and …
biogeochemistry for the European Copernicus Services. The system integrates model and …
Effective variational data assimilation in air-pollution prediction
Numerical simulations are widely used as a predictive tool to better understand complex air
flows and pollution transport on the scale of individual buildings, city blocks, and entire …
flows and pollution transport on the scale of individual buildings, city blocks, and entire …
Exploration of OpenCL heterogeneous programming for porting solidification modeling to CPU‐GPU platforms
This article provides a comprehensive study of OpenCL heterogeneous programming for
porting applications to CPU–GPU computing platforms, with a real‐life application for the …
porting applications to CPU–GPU computing platforms, with a real‐life application for the …
Model reduction in space and time for ab initio decomposition of 4D variational data assimilation problems
L D'Amore, R Cacciapuoti - Applied Numerical Mathematics, 2021 - Elsevier
We present an innovative approach for solving time dependent Four Dimensional
Variational Data Assimilation (4D VAR DA) problems. The proposed approach performs a …
Variational Data Assimilation (4D VAR DA) problems. The proposed approach performs a …
Predicting multidimensional data via tensor learning
G Brandi, T Di Matteo - Journal of Computational Science, 2021 - Elsevier
The analysis of multidimensional data is becoming a more and more relevant topic in
statistical and machine learning research. Given their complexity, such data objects are …
statistical and machine learning research. Given their complexity, such data objects are …
A resource-efficient model for deep kernel learning
L D'Amore - arXiv preprint arXiv:2410.09926, 2024 - arxiv.org
According to the Hughes phenomenon, the major challenges encountered in computations
with learning models comes from the scale of complexity, eg the so-called curse of …
with learning models comes from the scale of complexity, eg the so-called curse of …
Space-Time Decomposition of Kalman Filter
L D'Amore, R Cacciapuoti - arXiv preprint arXiv:2312.00007, 2023 - arxiv.org
We present an innovative interpretation of Kalman Filter (KF, for short) combining the ideas
of Schwarz Domain Decomposition (DD) and Parallel in Time (PinT) approaches. Thereafter …
of Schwarz Domain Decomposition (DD) and Parallel in Time (PinT) approaches. Thereafter …