Submitted

4. O. Davis and M. Motamed. Approximation power of deep neural networks: an explanatory mathematical survey, 2024.   PDF

3. M. Motamed and N. A. Petersson. Gaussian process-based Bayesian quantum characterization, 2024.   PDF

2. Z. Peng, D. Appelo, N. A. Petersson, M. Motamed, F. Garcia, and Y. Cho. Deterministic and Bayesian characterization of quantum computing devices, 2023.   PDF

1. O. Davis, M. Motamed, and R. Tempone. Residual Multi-Fidelity Neural Network Computing, 2023.   PDF

Refereed Journals

21. O. Davis, G. Geraci, and M. Motamed. Deep Learning without Global Optimization by Random Fourier Neural Networks. SIAM J. Scientific Computing, 2024.   PDF

20. S. Middleton and M. Motamed. A complete shortest independent loop set algorithm for model structure analysis. System Dynamics Review, 2024.   PDF

19. M. Motamed. A hierarchically low-rank optimal transport dissimilarity measure for structured data. BIT Numerical Mathematics, vol. 62, pp. 1945-1982, 2022.   PDF

18. M. Motamed. A multi-fidelity neural network surrogate sampling method for uncertainty quantification. International J. for Uncertainty Quantification, vol. 10, pp. 315-332, 2020.   PDF

17. M. Motamed and D. Appelo. Wasserstein metric-driven Bayesian inversion with applications to signal processing. International J. for Uncertainty Quantification, vol. 9, pp. 395-414, 2019.   PDF

16. M. Motamed. Fuzzy-Stochastic Partial Differential Equations. SIAM J. Uncertainty Quantification, vol. 7, pp. 1076-1104, 2019.   PDF

15. M. Motamed. Generalization of Kreiss Theory to Hyperbolic Problems with Boundary-type Eigenmodes. Communications in Mathematical Sciences, vol. 17, pp. 669-703, 2019.   PDF

14. M. Motamed and D. Appelo. A Multi-Order Discontinuous Galerkin Monte Carlo Method for Hyperbolic Problems with Stochastic parameters. SIAM J. Numerical Analysis, vol. 56, pp. 448-468, 2018.   PDF

13. G. Malenova and M. Motamed and O. Runborg. Stochastic Regularity of a Quadratic Observable of High Frequency Waves. Research in the Mathematical Sciences, 4:3, 2017.  PDF

12. G. Malenova and M. Motamed and O. Runborg and R. Tempone. A Sparse Stochastic Collocation Technique for High Frequency Wave Propagation with Uncertainty. SIAM J. Uncertainty Quantification, vol. 4, pp. 1084-1110, 2016.  PDF

11. I. Babuska and M. Motamed. A Fuzzy-Stochastic Multiscale Model for Fiber Composites: A One-Dimensional Study. Computer Methods in Applied Mechanics and Engineering, vol. 302, pp. 109-130, 2016.  PDF

10. M. Motamed and F. Nobile and R. Tempone. Analysis and Computation of the Elastic Wave Equation with Random Coefficients. Computers and Mathematics with Applications, vol. 70, pp. 2454-2473, 2015.  PDF

9. Q. Long and M. Motamed and R. Tempone. Fast Bayesian Optimal Experimental Design for Seismic Source Inversion. Computer Methods in Applied Mechanics and Engineering, vol. 291, pp. 123-145, 2015.  PDF

8. M. Motamed and O. Runborg. A Wavefront-Based Gaussian Beam Method for Computing High Frequency Wave Propagation Problems. Computers and Mathematics with Applications, vol. 69, pp. 949-963, 2015.  PDF

7. I. Babuska and M. Motamed and R. Tempone. A stochastic multiscale method for the elastodynamic wave equations arising from fiber composites. Computer Methods in Applied Mechanics and Engineering, vol. 276, pp. 190-211, 2014.  PDF

6. M. Motamed and F. Nobile and R. Tempone. A Stochastic Collocation Method for the Second Order Wave Equation with a Discontinuous Random Speed. Numerische Mathematik, vol. 123, no. 3, pp. 493-536, 2013.  PDF

5. M. Motamed and C. B. Macdonald and S. J. Ruuth. On the Linear Stability of the Fifth-Order WENO Discretization. Journal of Scientific Computing, vol. 47, no. 2, pp. 127-149, 2011.  PDF

4. M. Motamed and O. Runborg. Taylor Expansion and Discretization Errors in Gaussian Beam Superposition. Wave Motion, vol. 47, no. 7, pp. 421-439, 2010.  PDF

3. M. Motamed and O. Runborg. A Multiple-Patch Phase Space Method for Computing Trajectories on Manifolds with Applications to Wave Propagation Problems. Communications in Mathematical Sciences, vol. 5, no. 3, pp. 617-648, 2007.  PDF

2. M. Motamed and O. Runborg. A Fast Phase Space Method for Computing Creeping Rays. Journal of Computational Physics, vol. 219, issue 1, pp. 276-295, 2006.  PDF

1. M. Motamed, M. Babiuc, B. Szilagyi, H-O. Kreiss, and J. Winicour. Finite Difference Schemes for Second Order Systems Describing Black Holes. Journal of Physical Review D, vol. 73, issue 12, 2006.  PDF

Chapter in Books, Conference Proceedings, Theses

7. M. Motamed and O. Runborg. Asymptotic Approximations of High Frequency Wave Propagation Problems. In Highly Oscillatory Problems, volume 366 of London Mathematical Society Lecture Note Series, Cambridge University Press, pp. 72-97, 2009.

6. M. Motamed. Topics in Analysis and Computation of Linear Wave Propagation. Doctoral Thesis. School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden, 2008. ISBN 978-91-7178-961-7.

5. M. Motamed and O. Runborg. Solution of High-Frequency Wave Propagation Problems by a Fast Multiple-Patch Phase Space Method. In Proceedings of WAVES 2007, University of Reading, UK, 2007.

4. M. Motamed and O. Runborg. A Wavefront Gaussian Beam Method for High Frequency Wave Propagation. In Proceedings of WAVES 2007, University of Reading, UK, 2007.

3. M. Motamed. Phase Space Methods for Computing Creeping Rays. Licentiate Thesis. School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden, 2006. ISBN 91-7178-467-5.

2. M. Motamed and O. Runborg. A Fast Method for the Creeping Ray Contribution to Scattering Problems. In Proceedings of 2nd Conference on Mathematical Modeling of Wave Phenomena, Vaxjo, Sweden, AIP Conference Proceedings, pp. 56-64, 2005.

1. M. Motamed. PML Methods for Aero Acoustics Computations. Master's Thesis. Department of Numerical Analysis and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden, 2003. TRITA-NA-E03108.

Unpublished Preprints

2. I. Babuska and M. Motamed. Neumann Recovery Technique in Numerical Homogenization, 2016.

1. I. Babuska and M. Motamed. Fuzzy Set Theory and Fuzzy Randomness: A New Computational Approach, 2016.

motamed@math.unm.edu
Last updated: 2023