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PENG9570 Final Report: 06 References

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Writing Project:: PENG9570 Course Project - Writing Project - VIBE Draft Index:: Drafts - PENG9570 Course Project - VIBE Outline Partner:: 06 - References - Outline 1 Previous Draft:: 06 - References - Draft 6 - HUMAN Role:: Final reference draft adapted from Overleaf final modifications

6. References

Primary Sources

FEM, PDEs, and Sparse Solvers

  • Brenner, S. C. and Scott, L. R. (2008). The Mathematical Theory of Finite Element Methods, 3rd ed. Springer.
  • Larson, M. G. and Bengzon, F. (2013). The Finite Element Method: Theory, Implementation, and Applications. Springer.
  • Evans, L. C. (2010). Partial Differential Equations, 2nd ed. AMS.
  • Saad, Y. (2003). Iterative Methods for Sparse Linear Systems, 2nd ed. SIAM.
  • Hestenes, M. R. and Stiefel, E. (1952). Methods of conjugate gradients for solving linear systems. J. Res. NBS 49, 409-436.
  • Saad, Y. and Schultz, M. H. (1986). GMRES. SIAM J. Sci. Stat. Comput. 7, 856-869.
  • Shewchuk, J. R. (1996). Triangle: Engineering a 2D quality mesh generator and Delaunay triangulator. In Applied Computational Geometry Towards Geometric Engineering, 203-222. Springer.

Spiking Networks, Control, and Hardware Context

  • Boerlin, M., Machens, C. K. and Deneve, S. (2013). Predictive coding of dynamical variables in balanced spiking networks. PLoS Comput. Biol. 9, e1003258.
  • Brette, R. (2015). Philosophy of the spike. Front. Syst. Neurosci. 9, 151.
  • Astrom, K. J. and Murray, R. M. (2008). Feedback Systems. Princeton University Press.
  • Hairer, E., Norsett, S. P. and Wanner, G. (1993). Solving Ordinary Differential Equations I. Springer.
  • Davies, M. et al. (2021). Advancing neuromorphic computing with Loihi. Proc. IEEE 109, 911-934.
  • Orchard, G. et al. (2021). Efficient neuromorphic signal processing with Loihi 2. IEEE SiPS, 254-259.
  • Boahen, K. A. (2017). A neuromorph’s prospectus. Computing in Science and Engineering 19, 14-28.
  • Smith, J. D. et al. (2022). Neuromorphic scaling advantages for energy-efficient random walk computations. Nature Electronics 5, 102-112. DOI: https://doi.org/10.1038/s41928-021-00705-7.
  • Kudithipudi, D. et al. (2025). Neuromorphic computing at scale. Nature 637, 801-812.
  • Intel (2024). Intel builds world’s largest neuromorphic system to enable more sustainable AI. Intel Newsroom.
  • Anzt, H. et al. (2020). Preparing sparse solvers for exascale computing. Phil. Trans. R. Soc. A 378, 20190053.
  • Kogge, P. and Shalf, J. (2013). Exascale computing trends: adjusting to the “new normal” for computer architecture. Computing in Science and Engineering 15, 16-26.

Course and Local Materials