The MCTDH Package. MCTDH stands for Multi Configuration Time Dependent Hartree. MCTDH is a general algorithm to solve the time-dependent Schrödinger equation for multidimensional dynamical systems consisting of distinguishable particles. MCTDH can thus determine the quantal motion of the nuclei of a molecular system evolving on one or several coupled electronic potential energy surfaces. MCTDH by its very nature is an approximate method. However, it can be made as accurate as any competing method, but its numerical efficiency deteriorates with growing accuracy.

References in zbMATH (referenced in 36 articles )

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  1. Ceruti, Gianluca; Lubich, Christian: An unconventional robust integrator for dynamical low-rank approximation (2022)
  2. Iwen, Mark A.; Needell, Deanna; Rebrova, Elizaveta; Zare, Ali: Lower memory oblivious (tensor) subspace embeddings with fewer random bits: modewise methods for least squares (2021)
  3. Kazashi, Yoshihito; Nobile, Fabio; Vidličková, Eva: Stability properties of a projector-splitting scheme for dynamical low rank approximation of random parabolic equations (2021)
  4. Nüske, Feliks; Gelß, Patrick; Klus, Stefan; Clementi, Cecilia: Tensor-based computation of metastable and coherent sets (2021)
  5. Ashraphijuo, Morteza; Wang, Xiaodong: Characterization of sampling patterns for low-tt-rank tensor retrieval (2020)
  6. Patil, Prerna; Babaee, Hessam: Real-time reduced-order modeling of stochastic partial differential equations via time-dependent subspaces (2020)
  7. Uschmajew, André; Vandereycken, Bart: Geometric methods on low-rank matrix and tensor manifolds (2020)
  8. Exl, Lukas: An optimization approach for dynamical Tucker tensor approximation (2019)
  9. Gavrilyuk, Ivan; Khoromskij, Boris N.: Quasi-optimal rank-structured approximation to multidimensional parabolic problems by Cayley transform and Chebyshev interpolation (2019)
  10. Kieri, Emil; Vandereycken, Bart: Projection methods for dynamical low-rank approximation of high-dimensional problems (2019)
  11. Koch, Othmar: Convergence of exponential Lawson-multistep methods for the MCTDHF equations (2019)
  12. Lan, Jinchun; Zhang, Qianlong; Wei, Sha; Peng, Zhike; Dong, Xinjian; Zhang, Wenming: Uncertainty quantification for stochastic dynamical systems using time-dependent stochastic bases (2019)
  13. Carrington, Tucker: Iterative methods for computing vibrational spectra (2018)
  14. Musharbash, Eleonora; Nobile, Fabio: Dual dynamically orthogonal approximation of incompressible Navier Stokes equations with random boundary conditions (2018)
  15. Babaee, Hessam; Choi, Minseok; Sapsis, Themistoklis P.; Karniadakis, George Em: A robust bi-orthogonal/dynamically-orthogonal method using the covariance pseudo-inverse with application to stochastic flow problems (2017)
  16. Burkhard Schmidt, Carsten Hartmann: WavePacket: A Matlab package for numerical quantum dynamics. II: Open quantum systems, optimal control, and model reduction (2017) arXiv
  17. Burkhard Schmidt, Ulf Lorenz: WavePacket: A Matlab package for numerical quantum dynamics. I: Closed quantum systems and discrete variable representations (2017) arXiv
  18. Rauhut, Holger; Schneider, Reinhold; Stojanac, Željka: Low rank tensor recovery via iterative hard thresholding (2017)
  19. Bachmayr, Markus; Schneider, Reinhold; Uschmajew, André: Tensor networks and hierarchical tensors for the solution of high-dimensional partial differential equations (2016)
  20. Lubich, Christian; Oseledets, Ivan V.; Vandereycken, Bart: Time integration of tensor trains (2015)

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