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Instead, the method solves a sequence of reduced Galerkin problems, which can be set up efficiently due to the TT decomposition of the right-hand side.\nThe reduced system allows a fast estimation of the time discretization error, and hence adaptation of the time steps.\nBesides, conservation laws can be preserved exactly in the reduced model by expanding the approximation subspace with the generating vectors of the linear invariants and correction of the Euclidean norm.\nIn numerical experiments with the transport and the chemical master equations,\nwe demonstrate that the new method is faster than traditional time stepping and stochastic simulation algorithms, whereas the invariants are preserved up to the machine precision irrespectively of the TT approximation accuracy.<\/jats:p>","DOI":"10.1515\/cmam-2018-0023","type":"journal-article","created":{"date-parts":[[2018,9,11]],"date-time":"2018-09-11T09:01:39Z","timestamp":1536656499000},"page":"23-38","source":"Crossref","is-referenced-by-count":31,"title":["A Tensor Decomposition Algorithm for Large ODEs with Conservation Laws"],"prefix":"10.1515","volume":"19","author":[{"given":"Sergey V.","family":"Dolgov","sequence":"first","affiliation":[{"name":"University of Bath , Claverton Down, BA2 7AY , Bath , United Kingdom"}]}],"member":"374","published-online":{"date-parts":[[2018,9,11]]},"reference":[{"key":"2023033110133767969_j_cmam-2018-0023_ref_001_w2aab3b7d799b1b6b1ab2ab1Aa","doi-asserted-by":"crossref","unstructured":"A. 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