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Research Interests
1. Advanced 4-D Var Data-Assimilation Methods
The objective of this research effort which has been sponsored by NSF and NASA for many years is to use the mathematical theory of optimal control of partial differential equations for distributed parameter systems.
2. Large-Scale Minimization
Methods for large scale constrained and unconstrained minimization are developed designed to mimic the behavior of variable-metric quasi-Newton methods which have an enhanced convergence rate. In these memoryless methods the Hessian matrix is updated but not stored. Current research work on these methods includes tests of the bundle algorithm for minimization of discontinuous functions and work on impact of efficient large-scale minimization algorithms ( L-BFGS, Hessian free methods and hybrid methods applied to multidisciplinary problems).
3. Ensemble Kalman filter methods
In collaboration with the group of Milija Zupanski and Prof Navon, are developing ensemble aspects related to forecast error covariance, balance constraints, , nonlinearity, model errors, computational efficiency, and verification. In particular, the results obtained using a version of Maximum Likelihood Ensemble Filter (MLEF) is considered.
1. Advanced 4-D Var Data-Assimilation Methods
The objective of this research effort which has been sponsored by NSF and NASA for many years is to use the mathematical theory of optimal control of partial differential equations for distributed parameter systems.
2. Large-Scale Minimization
Methods for large scale constrained and unconstrained minimization are developed designed to mimic the behavior of variable-metric quasi-Newton methods which have an enhanced convergence rate. In these memoryless methods the Hessian matrix is updated but not stored. Current research work on these methods includes tests of the bundle algorithm for minimization of discontinuous functions and work on impact of efficient large-scale minimization algorithms ( L-BFGS, Hessian free methods and hybrid methods applied to multidisciplinary problems).
3. Ensemble Kalman filter methods
In collaboration with the group of Milija Zupanski and Prof Navon, are developing ensemble aspects related to forecast error covariance, balance constraints, , nonlinearity, model errors, computational efficiency, and verification. In particular, the results obtained using a version of Maximum Likelihood Ensemble Filter (MLEF) is considered.
研究兴趣
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JOURNAL OF COMPUTATIONAL PHYSICS (2024): 112600-112600
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERINGno. 13 (2023): 3087-3111
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SSRN Electronic Journal (2023)
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Computers & Fluids (2023): 105862-105862
J. Comput. Sci. (2023): 102024-102024
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