Dr. Akwum Onwunta
Max Planck Institute for Dynamics of Complex Technical Systems
PDEs with stochastic coefficients
Optimal control problems with random inputs
Numerical linear algebra
Tensor-based algorithms for high-dimensional systems
Quantitative credit risk modeling
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.