Publications by Author: Sandu
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Order by: [Title], [Author], [Editor], [Year]
D. N. Daescu, G. R. Carmichael, A. Sandu
Adjoint Implementation of Rosenbrock Methods Applied to Variational Data Assimilation Problems
Article in Journal of Computational Physics, 2000
Application Area:
Atmospheric Chemistry
Tools:
TAMC
Theory & Techniques:
Adjoint
A. Sandu, W. Liao, G. R. Carmichael, D. Henze, J. H. Seinfeld, T. Chai, D. Daescu
Computational aspects of data assimilation for areosol dynamics
Conference proceeding, Computational Science - ICCS 2004: 4th International Conference, Kraków, Poland, June 6-9, 2004, Proceedings, Part III, Springer, 2004
Application Area:
Atmospheric Chemistry
Tools:
TAMC
Mihai Alexe, Adrian Sandu
Forward and adjoint sensitivity analysis with continuous explicit Runge-Kutta schemes
Article in Applied Mathematics and Computation, 2009
not yet classified
A. Sandu, W. Liao, G. R. Carmichael, D. K. Henze, J. H. Seinfeld
Inverse Modeling Of Aerosol Dynamics Using Adjoints: Theoretical And Numerical Considerations
Article in Aerosol Science And Technology, 2005
Application Area:
Atmospheric Chemistry
Tools:
TAMC
D. K. Henze, J. H. Seinfeld, W. Liao, A. Sandu, G. R. Carmichael
Inverse Modeling of Aerosol Dynamics: Condensational Growth
Article in J. Geophys. Res, 2004
Application Area:
Meteorology
Tools:
TAMC
Theory & Techniques:
Adjoint
Mihai Alexe, Adrian Sandu
On the discrete adjoints of adaptive time stepping algorithms
Article in Journal of Computational and Applied Mathematics, 2009
Theory & Techniques:
Adjoint
Adrian Sandu
On the Properties of Runge-Kutta Discrete Adjoints
Conference proceeding, Computational Science -- ICCS 2006, Springer, 2006
Theory & Techniques:
Adjoint
Adrian Sandu
Reverse Automatic Differentiation of Linear Multistep Methods
Advances in Automatic Differentiation, Springer, 2008
Theory & Techniques:
Adjoint
Ulrich Römer, Mahesh Narayanamurthi, Adrian Sandu
Solving parameter estimation problems with discrete adjoint exponential integrators
Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018
not yet classified

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