Prof. Dr. Melina Freitag

Professorin

Kontakt
Raum:
2.09.2.09
Telefon:
+49 331 977-203121
...
  • Numerical linear algebra, in particular iterative methods for eigenvalue problems and linear systems, Krylov subspace methods.
  • Model order reduction, matrix theory and applications.
  • Data assimilation and inverse problems.

Publications

2024 | Nonintrusive model order reduction for stochastic differential equations | M. A. Freitag, J. M. Nicolaus, M. RedmannZeitschrift: arXivLink zum Preprint

Nonintrusive model order reduction for stochastic differential equations

Autoren: M. A. Freitag, J. M. Nicolaus, M. Redmann (2024)

A non-intrusive model order reduction method for bilinear stochastic differential equations with additive noise is proposed. A reduced order model (ROM) is designed in order to approximate the statistical properties of high-dimensional systems. The drift and diffusion coefficients of the ROM are inferred from state observations by solving appropriate least-squares problems. The closeness of the ROM obtained by the presented approach to the intrusive ROM obtained by the proper orthogonal decomposition (POD) method is investigated. Two generalisations of the snapshot-based dominant subspace construction to the stochastic case are presented. Numerical experiments are provided to compare the developed approach to POD.

Zeitschrift:
arXiv

2024 | Low-rank solutions to the stochastic Helmholtz equation | A. Kaya, M.A. FreitagZeitschrift: Journal of Computational and Applied MathematicsSeiten: In PressLink zur Publikation , Link zum Preprint

Low-rank solutions to the stochastic Helmholtz equation

Autoren: A. Kaya, M.A. Freitag (2024)

In this paper, we consider low-rank approximations for the solutions to the stochastic Helmholtz equation with random coefficients. A Stochastic Galerkin finite element method is used for the discretization of the Helmholtz problem. Existence theory for the low-rank approximation is established when the system matrix is indefinite. The low-rank algorithm does not require the construction of a large system matrix which results in an advantage in terms of CPU time and storage. Numerical results show that, when the operations in a low-rank method are performed efficiently, it is possible to obtain an advantage in terms of storage and CPU time compared to computations in full rank. We also propose a general approach to implement a preconditioner using the low-rank format efficiently.

Zeitschrift:
Journal of Computational and Applied Mathematics
Seiten:
In Press

2024 | Optimal Sparse Energy Sampling for X-ray Spectro-Microscopy: Reducing the X-ray Dose and Experiment Time Using Model Order Reduction | P.D. Quinn, M.S. Landman, T. Davis, M.A. Freitag, S. Gazzola, S. DolgovZeitschrift: Chem. Biomed. ImagingVerlag: ACS PublicationsLink zur Publikation ,

https://doi.org/10.1021/cbmi.3c00116

Optimal Sparse Energy Sampling for X-ray Spectro-Microscopy: Reducing the X-ray Dose and Experiment Time Using Model Order Reduction

Autoren: P.D. Quinn, M.S. Landman, T. Davis, M.A. Freitag, S. Gazzola, S. Dolgov (2024)

The application of X-ray spectro-microscopy to image changes in the chemical state in application areas such as catalysis, environmental science, or biological samples can be limited by factors such as the speed of measurement, the presence of dilute concentrations, radiation damage, and thermal drift during the measurement. We have adapted a reduced-order model approach, known as the discrete empirical interpolation method, which identifies how to optimally subsample the spectroscopic information, accounting for background variations in the signal, to provide an accurate approximation of an equivalent full spectroscopic measurement from the sampled material. This approach uses readily available prior information to guide and significantly reduce the sampling requirements impacting both the total X-ray dose and the acquisition time. The reduced-order model approach can be adapted more broadly to any spectral or spectro-microscopy measurement where a low-rank approximation can be made from prior information on the possible states of a system, and examples of the approach are presented.

Zeitschrift:
Chem. Biomed. Imaging
Verlag:
ACS Publications

2023 | Sparse grid based Chebyshev HOPGD for parameterized linear systems | S. Correnty, M.A. Freitag, and K. SoodhalterZeitschrift: arXivReihe: 2309.14178Link zum Preprint

Sparse grid based Chebyshev HOPGD for parameterized linear systems

Autoren: S. Correnty, M.A. Freitag, and K. Soodhalter (2023)

Zeitschrift:
arXiv
Reihe:
2309.14178

2023 | Time-limited Balanced Truncation for Data Assimilation Problems | J. König, M.A. FreitagZeitschrift: Journal of Scientific ComputingSeiten: 22. Article No.: 47Band: 97Link zur Publikation , Link zum Preprint

Time-limited Balanced Truncation for Data Assimilation Problems

Autoren: J. König, M.A. Freitag (2023)

Balanced truncation is a well-established model order reduction method in system theory that has been applied to a variety of problems. Recently, a connection between linear Gaussian Bayesian inference problems and the system theoretic concept of balanced truncation was drawn for the first time. Although this connection is new, the application of balanced truncation to data assimilation is not a novel concept: It has already been used in four-dimensional variational data assimilation (4D-Var) in its discrete formulation. In this paper, the link between system theory  and data assimilation is further strengthened by discussing the application of balanced truncation to standard linear Gaussian Bayesian inference, and, in particular, the 4D-Var method.  similarities between both data assimilation problems allow a discussion of established methods as well as a generalisation of the state-of-the-art approach to arbitrary prior covariances as  reachability Gramians. Furthermore, we propose an enhanced approach using time-limited balanced truncation that allows to balance Bayesian inference for unstable systems and in addition mproves the numerical results for short observation periods.

Zeitschrift:
Journal of Scientific Computing
Seiten:
22. Article No.: 47
Band:
97

2023 | Model order reduction methods applied to neural network training | M.A. Freitag, J.M. Nicolaus, M. RedmannZeitschrift: Proceedings in Applied Mathematics and MechanicsSeiten: e202300078Link zur Publikation

Model order reduction methods applied to neural network training

Autoren: M.A. Freitag, J.M. Nicolaus, M. Redmann (2023)

Neural networks have emerged as powerful and versatile tools in the field of deep learning. As the complexity of the task increases, so do size and architectural complexity of the causing compression techniques to become a focus of current research. Parameter truncation can provide a significant reduction in memory and computational complexity. Originating from a model order reduction framework, the Discrete Empirical Interpolation Method is applied to the gradient descent training of neural networks and analyze for important parameters. The approach for various state-of-the-art neural networks is compared to established truncation methods. Further metrics like L2 and Cross-Entropy Loss, as well as accuracy and compression rate are reported.

Zeitschrift:
Proceedings in Applied Mathematics and Mechanics
Seiten:
e202300078

2023 | Time-limited Balanced Truncation within Incremental Four-Dimensional Variational Data Assimilation | J. König, M.A. FreitagZeitschrift: Proceedings in Applied Mathematics and MechanicsSeiten: e202300019Link zur Publikation

Time-limited Balanced Truncation within Incremental Four-Dimensional Variational Data Assimilation

Autoren: J. König, M.A. Freitag (2023)

Four-dimensional variational data assimilation (4D-Var) is a data assimilation method often used in weather forecasting. Based on a numerical model and observations of a system, it predicts the system state beyond the last time of measurement. This requires the minimisation of a functional. At each step of the optimisation algorithm, a full nonlinear model evaluation and its adjoint is required. This quickly becomes very costly, especially in high dimensions. For this reason, a surrogate model is needed that approximates the full model well, but requires significantly less computational effort. In this paper, we propose time-limited balanced truncation to build such a reduced-order model. Our approach is able to deal with unstable system matrices. We demonstrate its performance in experiments and compare it with α-bounded balanced truncation, which is an another reduction approach for unstable systems.

Zeitschrift:
Proceedings in Applied Mathematics and Mechanics
Seiten:
e202300019

2023 | Can one hear the depth of the water? | M.A. Freitag, P.Kriz, T. Mach, J. M. NicolausZeitschrift: Proceedings in Applied Mathematics and MechanicsSeiten: e202300122Link zur Publikation

Can one hear the depth of the water?

Autoren: M.A. Freitag, P.Kriz, T. Mach, J. M. Nicolaus (2023)

We discuss discrete-time dynamical systems depending on a parameter μ. Assuming that the system matrix A(μ) is given, but the parameter μ is unknown, we infer the most-likely parameter μm≈μ from an observed trajectory x of the dynamical system. We use parametric eigenpairs (vi(μ),lambdai(μ) of the system matrix A(μ) computed with Newton's method based on a Chebyshev expansion. We then represent x in the eigenvector basis defined by the vi(μ) and compare the decay of the components with predictions based on the lambdai(μ). The resulting estimates for μ are combined using a kernel density estimator to find the most likely value for μm and a corresponding uncertainty quantification.

Zeitschrift:
Proceedings in Applied Mathematics and Mechanics
Seiten:
e202300122

2023 | POD-Galerkin reduced order models and physics-informed neural networks for solving inverse problems for the Navier-Stokes equations | S. Hijazi, M.A. Freitag, N. LandwehrZeitschrift: Advanced Modeling and Simulation in Engineering SciencesSeiten: 38 pagesBand: 10Link zur Publikation , Link zum Preprint

POD-Galerkin reduced order models and physics-informed neural networks for solving inverse problems for the Navier-Stokes equations

Autoren: S. Hijazi, M.A. Freitag, N. Landwehr (2023)

We present a Reduced Order Model (ROM) which exploits recent developments in Physics Informed Neural Networks (PINNs) for solving inverse problems for the Navier--Stokes equations (NSE). In the proposed approach, the presence of simulated data for the fluid dynamics fields is assumed. A POD-Galerkin ROM is then constructed by applying POD on the snapshots matrices of the fluid fields and performing a Galerkin projection of the NSE (or the modified equations in case of turbulence modeling) onto the POD reduced basis. A POD-Galerkin PINN ROM is then derived by introducing deep neural networks which approximate the reduced outputs with the input being time and/or parameters of the model. The neural networks incorporate the physical equations (the POD-Galerkin reduced equations) into their structure as part of the loss function. Using this approach, the reduced model is able to approximate unknown parameters such as physical constants or the boundary conditions. A demonstration of the applicability of the proposed ROM is illustrated by two cases which are the steady flow around a backward step and the unsteady turbulent flow around a surface mounted cubic obstacle.

Zeitschrift:
Advanced Modeling and Simulation in Engineering Sciences
Seiten:
38 pages
Band:
10

2023 | Solving the Parametric Eigenvalue Problem by Taylor Series and Chebyshev Expansion | T. Mach, M.A. FreitagZeitschrift: arXivBand: 2302.03661Link zum Preprint

Solving the Parametric Eigenvalue Problem by Taylor Series and Chebyshev Expansion

Autoren: T. Mach, M.A. Freitag (2023)

We discuss two approaches to solving the parametric (or stochastic) eigenvalue problem A(μ)λ(μ)=A(μ)v(μ). One of them uses a Taylor expansion and the other a Chebyshev expansion. The parametric eigenvalue problem assumes that the matrix A depends on a parameter μ, where μ might be a random variable. Consequently, the eigenvalues and eigenvectors are also functions of μ. We compute a Taylor approximation of these functions about μ₀ by iteratively computing the Taylor coefficients. The complexity of this approach is O(n³) for all eigenpairs, if the derivatives of A(μ) at μ₀ are given.  The Chebyshev expansion works similarly. We first find an initial approximation iteratively which we then refine with Newton's method.  This second method is more expensive but provides a good approximation over the whole interval of the expansion instead around a single point.
 
We present numerical experiments confirming the complexity and demonstrating that the approaches are capable of tracking eigenvalues at intersection points.  Further experiments shed light on the limitations of the Taylor expansion approach with respect to the distance from the expansion point μ₀.

Zeitschrift:
arXiv
Band:
2302.03661

2022 | Conditioning analysis for discrete Helmholtz problems | A. Kaya, M.A. FreitagZeitschrift: Computers & Mathematics with ApplicationsVerlag: ElsevierSeiten: 171‒182Band: 118Link zur Publikation

Conditioning analysis for discrete Helmholtz problems

Autoren: A. Kaya, M.A. Freitag (2022)

In this paper, we examine conditioning of the discretization of the Helmholtz problem. Although the discrete Helmholtz problem has been studied from different perspectives, to the best of our knowledge, there is no conditioning analysis for it. We aim to fill this gap in the literature. We propose a novel method in 1D to observe the near-zero eigenvalues of a symmetric indefinite matrix. Standard classification of ill-conditioning based on the matrix condition number is not true for the discrete Helmholtz problem. We relate the ill-conditioning of the discretization of the Helmholtz problem with the condition number of the matrix. We carry out analytical conditioning analysis in 1D and extend our observations to 2D with numerical observations. We examine several discretizations. We find different regions in which the condition number of the problem shows different characteristics. We also explain the general behavior of the solutions in these regions.

Zeitschrift:
Computers & Mathematics with Applications
Verlag:
Elsevier
Seiten:
171‒182
Band:
118

2022 | Datenassimilation: Die nahtlose Verschmelzung von Daten und Modellen | M.A. Freitag, S. ReichZeitschrift: Mitteilungen der Deutschen Mathematiker-VereinigungVerlag: De GruyterSeiten: 108‒112Band: 30Link zur Publikation

Datenassimilation: Die nahtlose Verschmelzung von Daten und Modellen

Autoren: M.A. Freitag, S. Reich (2022)

Der Sonderforschungsbereich SFB1294 „Datenassimilation: Die nahtlose Verschmelzung von Daten und Modellen“ wird seit 2017 von der Deutschen Forschungsgemeinschaft an der Universität of Potsdam gefördert und befindet sich gegenwärtig am Beginn der zweiten Förderphase. Gemeinsam mit den Projektpartnern der Humboldt Universität zu Berlin, der Technischen Universität Berlin, dem Weierstraß-Institut für Angewandte Analysis und Stochastik sowie dem Helmholtz-Zentrum Potsdam: Deutsches GeoForschungsZentrum GFZ, werden innerhalb des Verbundes sowohl die statistischen und mathematischen Grundlagen also auch mannigfaltige Anwendungen zeitabhängiger inverser Problemeuntersucht. In diesem Beitrag werden in kurzen Zügen sowohl die grundlegende mathematische Fragestellung also auch die aktuellen wissenschaftlichen Projekte des SFBs vorgestellt

Zeitschrift:
Mitteilungen der Deutschen Mathematiker-Vereinigung
Verlag:
De Gruyter
Seiten:
108‒112
Band:
30

2021 | Autonomous Exploration and Identification of High Performing Adsorbents using Active Learning | G. Donval, C. Hand, J. Hook, E. Dupont, M. S. Landman, M.A. Freitag, M. Lennox, T. DürenVerlag: submittedLink zum Preprint

Autonomous Exploration and Identification of High Performing Adsorbents using Active Learning

Autoren: G. Donval, C. Hand, J. Hook, E. Dupont, M. S. Landman, M.A. Freitag, M. Lennox, T. Düren (2021)

MOFs and COFs are porous materials with a large variety of applications including gas storage and separation. Synthesised in a modular fashion from distinct building blocks, a near infinite number of structures can be constructed and the properties of the material can be tailored for a specific application. While this modularity is a very attractive feature it also poses a challenge. Attempting to identify the best performing material(s) for a given application is experimentally intractable. Current research efforts combine molecular simulations and machine learning techniques to evaluate the simulated performance of hundreds of thousands of materials to identify top performing MOFs and COFs for a given application. These approaches typically rely on moderated brute-force screening which is still resource-intensive as typically between 70‒100 % of the hundreds of thousands of materials must be simulated to create a training set for the machine learning models used, restricting screening to relatively simple molecules. In this work we demonstrate our novel Bayesian mining approach to materials screening which allows 62‒92 % of the top 100 porous materials for a range of applications to be readily identified from large materials databases after only assessing less than one percent of all materials. This is a stark contrast to the 0‒1 % achieved by conventional brute-force screening where porous materials are just chosen at random during a high throughput screening. Through this accelerated virtual screening process, the identification of high performing materials can be used to more rapidly inform experimental efforts and hence lead to an acceleration of the entire research and development pipeline of porous materials.

Verlag:
submitted

2021 | Optimization based model order reduction for stochastic systems | M. Redmann, M.A. FreitagZeitschrift: Appl. Math. Comput., 398Link zur Publikation

Optimization based model order reduction for stochastic systems

Autoren: M. Redmann, M.A. Freitag (2021)

Zeitschrift:
Appl. Math. Comput., 398

2020 | Inexact methods for the low-rank solution to large-scale Lyapunov equations | P. Kürschner, M.A. FreitagZeitschrift: BIT, 60:1221-1259Link zur Publikation , Link zum Preprint

Inexact methods for the low-rank solution to large-scale Lyapunov equations

Autoren: P. Kürschner, M.A. Freitag (2020)

Zeitschrift:
BIT, 60:1221-1259

2020 | Numerical linear algebra in data assimilation | M.A. FreitagZeitschrift: GAMM MitteilungenLink zur Publikation

Numerical linear algebra in data assimilation

Autoren: M.A. Freitag (2020)

Zeitschrift:
GAMM Mitteilungen

2019 | Teaching of Computing to Mathematics Students: Programming and Discrete Mathematics | J. Betteridge, J.H. Davenport, M.A. Freitag, W. Heijtljes, S. Kynaston, G. Sankaran, G. TraustasonZeitschrift: Proceedings of the 3rd Conference on Computing Education Practice, 12, 1-4Link zur Publikation

Teaching of Computing to Mathematics Students: Programming and Discrete Mathematics

Autoren: J. Betteridge, J.H. Davenport, M.A. Freitag, W. Heijtljes, S. Kynaston, G. Sankaran, G. Traustason (2019)

Zeitschrift:
Proceedings of the 3rd Conference on Computing Education Practice, 12, 1-4

2019 | Projection methods for weak constraint variational data assimilation | M.A. Freitag, D.L.H. Green

Projection methods for weak constraint variational data assimilation

Autoren: M.A. Freitag, D.L.H. Green (2019)

2018 | GMRES convergence bounds for eigenvalue problems | M.A. Freitag, P. Kürschner, J. PestanaZeitschrift: Comput. Methods Appl. Math.Seiten: 203-222Band: 18(2)Link zur Publikation

GMRES convergence bounds for eigenvalue problems

Autoren: M.A. Freitag, P. Kürschner, J. Pestana (2018)

Zeitschrift:
Comput. Methods Appl. Math.
Seiten:
203-222
Band:
18(2)

2018 | Model reduction and approximation: theory and algorithms | M. A. FreitagZeitschrift: SIAM Rev.Seiten: 763–767Band: 60(3) (review of book)

Model reduction and approximation: theory and algorithms

Autoren: M. A. Freitag (2018)

Zeitschrift:
SIAM Rev.
Seiten:
763–767
Band:
60(3)
(review of book)

2018 | Balanced model order reduction for linear random dynamical systems driven by Lévy noise | M. Redmann, M.A. FreitagZeitschrift: To appear in J. Comput. Dyn.Link zum Preprint

Balanced model order reduction for linear random dynamical systems driven by Lévy noise

Autoren: M. Redmann, M.A. Freitag (2018)

Zeitschrift:
To appear in J. Comput. Dyn.

2018 | A low-rank approach to the solution of weak constraint variational data assimilation problems | M.A. Freitag, D.L.H. GreenZeitschrift: J. Comput. Phys.Seiten: 263-281Band: 357Link zur Publikation

A low-rank approach to the solution of weak constraint variational data assimilation problems

Autoren: M.A. Freitag, D.L.H. Green (2018)

Zeitschrift:
J. Comput. Phys.
Seiten:
263-281
Band:
357

2015 | Tuned preconditioners for inexact two-sided inverse and Rayleigh quotient iteration | M.A. Freitag, P. KürschnerZeitschrift: Numer. Linear Algebra Appl.Seiten: 175–196Band: 22(1)Link zur Publikation

Tuned preconditioners for inexact two-sided inverse and Rayleigh quotient iteration

Autoren: M.A. Freitag, P. Kürschner (2015)

Zeitschrift:
Numer. Linear Algebra Appl.
Seiten:
175–196
Band:
22(1)

2015 | The effect of numerical model error on data assimilation | S.E. Jenkins, C.J. Budd, M.A. Freitag, N.D. SmithZeitschrift: J. Comput. Appl. Math.Seiten: 567 – 588Band: 290Link zur Publikation

The effect of numerical model error on data assimilation

Autoren: S.E. Jenkins, C.J. Budd, M.A. Freitag, N.D. Smith (2015)

Zeitschrift:
J. Comput. Appl. Math.
Seiten:
567 – 588
Band:
290

2014 | A new approach for calculating the real stability radius | M.A. Freitag, A. SpenceZeitschrift: BITVerlag: SpringerSeiten: 381-400Band: 54(2)Link zur Publikation

A new approach for calculating the real stability radius

Autoren: M.A. Freitag, A. Spence (2014)

Zeitschrift:
BIT
Verlag:
Springer
Seiten:
381-400
Band:
54(2)

2014 | Calculating the $H_\infty$-norm using the implicit determinant method | M.A. Freitag, A Spence, P. Van DoorenZeitschrift: SIAM J. Matrix Anal. Appl.Seiten: 619-635Band: 35(2)Link zur Publikation

Calculating the $H_\infty$-norm using the implicit determinant method

Autoren: M.A. Freitag, A Spence, P. Van Dooren (2014)

Zeitschrift:
SIAM J. Matrix Anal. Appl.
Seiten:
619-635
Band:
35(2)

2014 | The computation of Jordan blocks in parameter-dependent matrices | R.O. Akinola, M.A. Freitag, A. SpenceZeitschrift: IMA J. Numer. Anal.Verlag: Oxford University PressSeiten: 955-976Band: 34(3)Link zur Publikation

The computation of Jordan blocks in parameter-dependent matrices

Autoren: R.O. Akinola, M.A. Freitag, A. Spence (2014)

Zeitschrift:
IMA J. Numer. Anal.
Verlag:
Oxford University Press
Seiten:
955-976
Band:
34(3)

2014 | The calculation of the distance to a nearby defective matrix | R.O. Akinola, M.A. Freitag, A. SpenceZeitschrift: Numer. Linear Algebra Appl.Seiten: 403-414Band: 21(3)Link zur Publikation

The calculation of the distance to a nearby defective matrix

Autoren: R.O. Akinola, M.A. Freitag, A. Spence (2014)

Zeitschrift:
Numer. Linear Algebra Appl.
Seiten:
403-414
Band:
21(3)

2013 | Large Scale Inverse Problems: Computational Methods and Applications in the Earth Sciences | M. Cullen, M.A. Freitag, S. Kindermann and R. Scheichl (Editors)Reihe: Radon Series on Computational and Applied Mathematics, Vol. 13Verlag: De Gruyter, BerlinLink zur Publikation

Large Scale Inverse Problems: Computational Methods and Applications in the Earth Sciences

Autoren: M. Cullen, M.A. Freitag, S. Kindermann and R. Scheichl (Editors) (2013)

Reihe:
Radon Series on Computational and Applied Mathematics, Vol. 13
Verlag:
De Gruyter, Berlin

2013 | Synergy of inverse problems and data assimilation techniques. Large Scale Inverse Problems | M.A. Freitag, R.W.E PotthastZeitschrift: Radon Ser. Comput. Appl. Math.Verlag: De GruyterBand: 13 (Preprint)

Synergy of inverse problems and data assimilation techniques. Large Scale Inverse Problems

Autoren: M.A. Freitag, R.W.E Potthast (2013)

Zeitschrift:
Radon Ser. Comput. Appl. Math.
Verlag:
De Gruyter
Band:
13

2013 | The Origin of Power-Law Emergent Scaling in Large Binary Networks | D.P. Almond, C.J. Budd, M.A. Freitag, G.W. Hunt, N.J. McCullen, N.D. SmithZeitschrift: Phys. ASeiten: 1004-1027Band: 392(4)Link zur Publikation , Link zum Preprint

The Origin of Power-Law Emergent Scaling in Large Binary Networks

Autoren: D.P. Almond, C.J. Budd, M.A. Freitag, G.W. Hunt, N.J. McCullen, N.D. Smith (2013)

Zeitschrift:
Phys. A
Seiten:
1004-1027
Band:
392(4)

2013 | Resolution of sharp fronts in the presence of model error in variational data assimilation | M.A. Freitag, N.K. Nichols, C.J. BuddZeitschrift: Q. J. R. Meteorol. Soc.Seiten: 742-757Band: 139Link zur Publikation

Resolution of sharp fronts in the presence of model error in variational data assimilation

Autoren: M.A. Freitag, N.K. Nichols, C.J. Budd (2013)

Zeitschrift:
Q. J. R. Meteorol. Soc.
Seiten:
742-757
Band:
139

2011 | A Newton-based method for the calculation of the distance to instability | M.A. Freitag, A. SpenceZeitschrift: Linear Alg. Appl.Seiten: 3189-3205Band: 435(12)Link zur Publikation

A Newton-based method for the calculation of the distance to instability

Autoren: M.A. Freitag, A. Spence (2011)

Zeitschrift:
Linear Alg. Appl.
Seiten:
3189-3205
Band:
435(12)

2011 | Regularization techniques for ill-posed inverse problems in data assimilation | M.A. Freitag, N.K. Nichols, C.J. BuddZeitschrift: Comput. & FluidsSeiten: 168-173Band: 46(1)Link zur Publikation

Regularization techniques for ill-posed inverse problems in data assimilation

Autoren: M.A. Freitag, N.K. Nichols, C.J. Budd (2011)

Zeitschrift:
Comput. & Fluids
Seiten:
168-173
Band:
46(1)

2010 | L1-regularisation for ill-posed problems in variational data assimilation | M.A. Freitag, N.K. Nichols, C.J. BuddZeitschrift: PAMMSeiten: 665-668Band: 10(1)Link zur Publikation

L1-regularisation for ill-posed problems in variational data assimilation

Autoren: M.A. Freitag, N.K. Nichols, C.J. Budd (2010)

Zeitschrift:
PAMM
Seiten:
665-668
Band:
10(1)

2009 | Shift-invert Arnoldi's method with preconditioned iterative solves | M.A. Freitag, A. SpenceZeitschrift: SIAM J. Matrix Anal. Appl.Seiten: 942-969Band: 31(3)Link zur Publikation

Shift-invert Arnoldi's method with preconditioned iterative solves

Autoren: M.A. Freitag, A. Spence (2009)

Zeitschrift:
SIAM J. Matrix Anal. Appl.
Seiten:
942-969
Band:
31(3)

2009 | The calculation of the distance to instability by the computation of a Jordan block | M. Freitag (joint work with A. Spence) Oberwolfach Report No. 37/2009. (Report)

The calculation of the distance to instability by the computation of a Jordan block

Autoren: M. Freitag (joint work with A. Spence) (2009)

Oberwolfach Report No. 37/2009. (Report)

2008 | Rayleigh quotient iteration and simplified Jacobi-Davidson with preconditioned iterative solves for generalised eigenvalue problems | M.A. Freitag, A. Spence, E. Vainikko (Gzipped PostScript), (PDF)

Rayleigh quotient iteration and simplified Jacobi-Davidson with preconditioned iterative solves for generalised eigenvalue problems

Autoren: M.A. Freitag, A. Spence, E. Vainikko (2008)

2008 | The Impact of Multiplication Operators on the Ill-Posedness of Inverse Problems | Melina Freitag 168 pages, VDM Verlag Dr. Müller Aktiengesellschaft & Co. KG (2008). (Available online here)

The Impact of Multiplication Operators on the Ill-Posedness of Inverse Problems

Autoren: Melina Freitag (2008)

168 pages, VDM Verlag Dr. Müller Aktiengesellschaft & Co. KG (2008). (Available online here)

2008 | Rayleigh Quotient iteration and simplified Jacobi-Davidson method with preconditioned iterative solves | M.A. Freitag, A. SpenceZeitschrift: Linear Alg. Appl.Verlag: ElsevierSeiten: 2049-2060Band: 428(8-9)Link zur Publikation

Rayleigh Quotient iteration and simplified Jacobi-Davidson method with preconditioned iterative solves

Autoren: M.A. Freitag, A. Spence (2008)

Zeitschrift:
Linear Alg. Appl.
Verlag:
Elsevier
Seiten:
2049-2060
Band:
428(8-9)

2008 | A tuned preconditioner for inexact inverse iteration applied to Hermitian eigenvalue problems | M.A. Freitag, A. SpenceZeitschrift: IMA J. Numer. Anal.Verlag: Oxford University PressSeiten: 522-551Band: 28(3)Link zur Publikation

A tuned preconditioner for inexact inverse iteration applied to Hermitian eigenvalue problems

Autoren: M.A. Freitag, A. Spence (2008)

Zeitschrift:
IMA J. Numer. Anal.
Verlag:
Oxford University Press
Seiten:
522-551
Band:
28(3)

2007 | Convergence theory for inexact inverse iteration applied to the generalised nonsymmetric eigenproblem | M.A. Freitag, A. SpenceZeitschrift: Electron. Trans. Numer. Anal.Seiten: 40-64Band: 28Link zum Preprint

Convergence theory for inexact inverse iteration applied to the generalised nonsymmetric eigenproblem

Autoren: M.A. Freitag, A. Spence (2007)

Zeitschrift:
Electron. Trans. Numer. Anal.
Seiten:
40-64
Band:
28

2007 | Inner-outer Iterative Methods for Eigenvalue Problems - Convergence and Preconditioning | M. A. Freitag PhD Thesis, Department of Mathematical Sciences, University of Bath (2007). (Gzipped PostScript), (PDF)

Inner-outer Iterative Methods for Eigenvalue Problems - Convergence and Preconditioning

Autoren: M. A. Freitag (2007)

PhD Thesis, Department of Mathematical Sciences, University of Bath (2007). (Gzipped PostScript), (PDF)

2007 | The Preissmann box scheme and its modification for transcritical flows | M.A. Freitag, K. W. MortonZeitschrift: Internat. J. Numer. Methods Engrg.Verlag: WileySeiten: 791-811Band: 70(7)Link zur Publikation

The Preissmann box scheme and its modification for transcritical flows

Autoren: M.A. Freitag, K. W. Morton (2007)

Zeitschrift:
Internat. J. Numer. Methods Engrg.
Verlag:
Wiley
Seiten:
791-811
Band:
70(7)

2007 | Convergence rates for inexact inverse iteration with application to preconditioned iterative solves | M.A. Freitag, A. SpenceZeitschrift: BITVerlag: SpringerSeiten: 27-44Band: 47(1)Link zur Publikation

Convergence rates for inexact inverse iteration with application to preconditioned iterative solves

Autoren: M.A. Freitag, A. Spence (2007)

Zeitschrift:
BIT
Verlag:
Springer
Seiten:
27-44
Band:
47(1)

2005 | Analytical and numerical studies on the influence of multiplication operators for the ill-posedness of inverse problems | M. Freitag, B. HofmannZeitschrift: J. Inverse Ill-Posed Probl.Seiten: 123-148Band: 13(2)Link zur Publikation

Analytical and numerical studies on the influence of multiplication operators for the ill-posedness of inverse problems

Autoren: M. Freitag, B. Hofmann (2005)

Zeitschrift:
J. Inverse Ill-Posed Probl.
Seiten:
123-148
Band:
13(2)

2004 | On the Influence of Multiplication Operators on the Ill-posedness of Inverse Problems | M. Freitag Diplomarbeit, Fakultät für Mathematik, Technische Universität Chemnitz (2004). (Available online here)

On the Influence of Multiplication Operators on the Ill-posedness of Inverse Problems

Autoren: M. Freitag (2004)

Diplomarbeit, Fakultät für Mathematik, Technische Universität Chemnitz (2004). (Available online here)

2003 | Transcritical flow modelling with the Box Scheme | M. Freitag MSc Thesis, Department of Mathematical Sciences, University of Bath (2003). (GZipped PostScript), (PDF)

Transcritical flow modelling with the Box Scheme

Autoren: M. Freitag (2003)

MSc Thesis, Department of Mathematical Sciences, University of Bath (2003). (GZipped PostScript), (PDF)