Dr. Akwum Onwunta

Computational Methods in Systems and Control Theory

Max Planck Institute for Dynamics of Complex Technical Systems

Uncertainty quantification

PDEs with stochastic coefficients

Optimal control problems with random inputs

Numerical linear algebra

Tensor-based algorithms for high-dimensional systems

Quantitative credit risk modeling

Selected publications:

Benner, P., Dolgov, S., Onwunta, A. and Stoll, M. (2016): Lifting the curse of dimensionality: Optimization of Navier-Stokes equations with uncertain inputs. In preparation.

Benner, P., Dolgov, S., Onwunta, A. and Stoll, M. (2016): Low-rank solvers for unsteady Stokes-Brinkman optimal control problem with random data, Computer Methods in Applied Mechanics and Engineering, 304, pp. 26 - 54

Benner, P., Onwunta, A. and Stoll, M. (2016): Block-diagonal preconditioning for optimal control problems constrained by PDEs with uncertain inputs, SIAM Journal on Matrix Analysis and Applications, 37 (2), pp. 491-518.

Benner, P., Onwunta, A. and Stoll, M. (2015): Low-rank solution of unsteady diffusion equations with stochastic coefficients, SIAM/ASA Journal on Uncertainty Quantification, 3 (1), pp. 622 - 649.

Lyra, M., Onwunta, A. and Winker, P. (2015): Threshold Accepting for credit risk assessment and validation, Journal of Banking Regulation, 16 (2) pp. 130 - 145.

Onwunta, A. (2011): Contributions to credit portfolio modeling and optimization. Peter Lang AG - International Academic Publishers, Frankfurt, Germany.

Kalkbrener, M. and Onwunta, A. (2010). Validating structural credit portfolio models, In Roesch, D. and Scheule, H. (eds.), Model Risk: Identification, Measurement and Management, pp. 233 - 261, Risk Books, London.

Onwunta, A. (2016): Low-rank iterative solvers for large-scale stochastic Galerkin linear systems. Ph.D Thesis in Applied Mathematics, Otto von Guericke University, Magdeburg, Germany.

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