Automatic differentiation through the use of hyper-dual numbers for second derivatives. Automatic differentiation techniques are typically derived based on the chain rule of differentiation. Other methods can be derived based on the inherent mathematical properties of generalized complex numbers that enable first-derivative information to be carried in the non-real part of the number. These methods are capable of producing effectively exact derivative values. However, when second-derivative information is desired, generalized complex numbers are not sufficient. Higher-dimensional extensions of generalized complex numbers, with multiple non-real parts, can produce accurate second-derivative information provided that multiplication is commutative. One particular number system is developed, termed hyper-dual numbers, which produces exact first- and second-derivative information. The accuracy of these calculations is demonstrated on an unstructured, parallel, unsteady Reynolds-averaged Navier-Stokes solver.

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  1. Morra, Gabriele: Pythonic geodynamics. Implementations for fast computing (2018)
  2. Afzal, Asif; Ansari, Zahid; Rimaz Faizabadi, Ahmed; Ramis, M.K.: Parallelization strategies for computational fluid dynamics software: state of the art review (2017)
  3. Botbol, Vincent; Chailloux, Emmanuel; Le Gall, Tristan: Static analysis of communicating processes using symbolic transducers (2017)
  4. Calandra, H.; Gratton, S.; Vasseur, X.: A geometric multigrid preconditioner for the solution of the Helmholtz equation in three-dimensional heterogeneous media on massively parallel computers (2017)
  5. Schutte, Aaron D.: A nilpotent algebra approach to Lagrangian mechanics and constrained motion (2017)
  6. Briggs, J.P.; Pennycook, S.J.; Fergusson, J.R.; Jäykkä, J.; Shellard, E.P.S.: Separable projection integrals for higher-order correlators of the cosmic microwave sky: acceleration by factors exceeding 100 (2016)
  7. Fannon, James; Loiseau, Jean-Christophe; Valluri, Prashant; Bethune, Iain; Náraigh, Lennon Ó.: High-performance computational fluid dynamics: a custom-code approach (2016)
  8. Ghosh, Swarnava; Suryanarayana, Phanish: Higher-order finite-difference formulation of periodic orbital-free density functional theory (2016)
  9. Len^otre, Lionel: A strategy for parallel implementations of stochastic Lagrangian simulation (2016)
  10. Sapetina, A.F.: Supercomputer-aided comparison of the efficiency of using different mathematical statements of the 3D geophysical problem (2016)
  11. Satarić, Bogdan; Slavnić, Vladimir; Belić, Aleksandar; Balaž, Antun; Muruganandam, Paulsamy; Adhikari, Sadhan K.: Hybrid OpenMP/MPI programs for solving the time-dependent Gross-Pitaevskii equation in a fully anisotropic trap (2016)
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  13. Titarev, V.A.; Utyuzhnikov, S.V.; Chikitkin, A.V.: Openmp + MPI parallel implementation of a numerical method for solving a kinetic equation (2016)
  14. Veit, Alexander; Merta, Michal; Zapletal, Jan; Lukáš, Dalibor: Efficient solution of time-domain boundary integral equations arising in sound-hard scattering (2016)
  15. White, Robert E.: Computational mathematics. Models, methods, and analysis with MATLAB and MPI (2016)
  16. Zhang, Wei; Cai, Xing: Solving 3D time-fractional diffusion equations by high-performance parallel computing (2016)
  17. Aldasoro, Unai; Escudero, Laureano F.; Merino, María; Monge, Juan F.; Pérez, Gloria: On parallelization of a stochastic dynamic programming algorithm for solving large-scale mixed $0-1$ problems under uncertainty (2015)
  18. Azadbakht, Keyvan; Serbanescu, Vlad; de Boer, Frank: High performance computing applications using parallel data processing units (2015)
  19. Beskos, Alexandros; Jasra, Ajay; Muzaffer, Ege A.; Stuart, Andrew M.: Sequential Monte Carlo methods for Bayesian elliptic inverse problems (2015)
  20. Casoni, E.; Jérusalem, A.; Samaniego, C.; Eguzkitza, B.; Lafortune, P.; Tjahjanto, D.D.; Sáez, X.; Houzeaux, G.; Vázquez, M.: Alya: computational solid mechanics for supercomputers (2015)

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