Zobrazeno 1 - 10
of 46
pro vyhledávání: '"A. K. Saibaba"'
Publikováno v:
Geoscientific Model Development, Vol 15, Pp 5547-5565 (2022)
Atmospheric inverse modeling describes the process of estimating greenhouse gas fluxes or air pollution emissions at the Earth's surface using observations of these gases collected in the atmosphere. The launch of new satellites, the expansion of sur
Externí odkaz:
https://doaj.org/article/9ab00c979cf245899c3d5c37f4cdfb47
Publikováno v:
Geoscientific Model Development, Vol 13, Pp 1771-1785 (2020)
Geostatistical inverse modeling (GIM) has become a common approach to estimating greenhouse gas fluxes at the Earth's surface using atmospheric observations. GIMs are unique relative to other commonly used approaches because they do not require a sin
Externí odkaz:
https://doaj.org/article/68883bd631a6403791184d6a96d177fe
Autor:
Hussam Al Daas, Grey Ballard, Paul Cazeaux, Eric Hallman, Agnieszka Międlar, Mirjeta Pasha, Tim W. Reid, Arvind K. Saibaba
Publikováno v:
SIAM Journal on Scientific Computing. 45:A74-A95
The Tensor-Train (TT) format is a highly compact low-rank representation for high-dimensional tensors. TT is particularly useful when representing approximations to the solutions of certain types of parametrized partial differential equations. For ma
Publikováno v:
Statistics and Computing. 32
Analyzing massive spatial datasets using Gaussian process model poses computational challenges. This is a problem prevailing heavily in applications such as environmental modeling, ecology, forestry and environmental heath. We present a novel approxi
Publikováno v:
Geoscientific Model Development, Vol 13, Pp 1771-1785 (2020)
Geostatistical inverse modeling (GIM) has become a common approach to estimating greenhouse gas fluxes at the Earth's surface using atmospheric observations. GIMs are unique relative to other commonly used approaches because they do not require a sin
Publikováno v:
SIAM Journal on Scientific Computing. 42:A1714-A1740
We consider optimal design of PDE-based Bayesian linear inverse problems with infinite-dimensional parameters. We focus on the A-optimal design criterion, defined as the average posterior variance and quantified by the trace of the posterior covarian
Atmospheric inverse modeling describes the process of estimating greenhouse gas fluxes or air pollution emissions at the Earth's surface using observations of these gases collected in the atmosphere. The launch of new satellites, the expansion of sur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::90ceb466bdb1f29c097a9170504db2a6
https://doi.org/10.5194/gmd-2021-393
https://doi.org/10.5194/gmd-2021-393
For real symmetric matrices that are accessible only through matrix vector products, we present Monte Carlo estimators for computing the diagonal elements. Our probabilistic bounds for normwise absolute and relative errors apply to Monte Carlo estima
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8dd4d9faa065135857b260f8550098e0
Autor:
Mohammad Farazmand, Arvind K. Saibaba
Reconstructing high-resolution flow fields from sparse measurements is a major challenge in fluid dynamics. Existing methods often vectorize the flow by stacking different spatial directions on top of each other, hence confounding the information enc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::97ded18956d2e59e95a09637e9396e05
Publikováno v:
SIAM/ASA Journal on Uncertainty Quantification. 7:1105-1131
Hierarchical models in Bayesian inverse problems are characterized by an assumed prior probability distribution for the unknown state and measurement error precision, and hyper-priors for the prior parameters. Combining these probability models using