Objective
Inverse problems that are governed by partial differential equations arise in many applications in computational science and engineering. Solving these large scale problems is a real challenge to the existing numerical methods, as they are generally highly ill-posed and non-convex. These difficulties are usually handled by introducing statistical Bayesian estimation methods that promote a-priori knowledge to the problems. Such methods address the uncertainties in the inverse problems, such as the noise and unknown parameters, so that the solution of the inverse problems is meaningful and realistic. Additionally, numerically solving these large-scale problems requires highly efficient and scalable numerical methods, which are still missing or inadequate for many applications.
Multiscale and multigrid methods are extremely efficient and scalable for some applications, but there are other problems that still pose severe challenges. In this research I plan to study two problems: one is the challenging inverse wave equation, which appears in many applications such as seismic exploration of reservoirs, and medical imaging. The other problem is the rather unexplored 4D imaging of flow in porous media, used for monitoring of reservoirs, carbon sequestration among other applications. I plan to develop efficient multiscale (and multigrid) methods for some of the key ingredients of the numerical solution of these problems. Such methods may enable a scalable and efficient solution of these inverse problems. In particular, to solve the inverse wave equation, one needs to efficiently solve the Helmholtz equation, which is still considered an open question. Another example is a multiscale approach for efficiently estimating an inverse of a covariance matrix given a few measurements. This problem lies in the heart of statistical Bayesian estimation methods, and it can be addressed if one considers the structure of the covariance for the problems of interest.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- natural sciences computer and information sciences computational science
- natural sciences mathematics pure mathematics mathematical analysis differential equations partial differential equations
- engineering and technology medical engineering diagnostic imaging
- natural sciences mathematics applied mathematics numerical analysis
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
FP7-PEOPLE-2013-IOF
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Funding Scheme
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Coordinator
84105 Beer Sheva
Israel
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.