更改

[[降阶建模]]（reduced-order modeling）中也少不了SVD的身影。降阶建模旨在减少复杂系统中的自由度数量。研究人员将SVD与径向基函数（radial basis functions）结合，用于插值三维非稳态流问题的解。<ref>{{citation | last1=Walton | first1=S. | last2=Hassan | first2=O. | last3=Morgan | first3=K. | date=2013 |url=https://linkinghub.elsevier.com/retrieve/pii/S0307904X13002771| title="Reduced order modelling for unsteady fluid flow using proper orthogonal decomposition and radial basis functions" | journal=Applied Mathematical Modelling | volume=37 | issue=20–21 | pages=8930–8945 | doi=10.1016/j.apm.2013.04.025}}</ref>

[[降阶建模]]（reduced-order modeling）中也少不了SVD的身影。降阶建模旨在减少复杂系统中的自由度数量。研究人员将SVD与径向基函数（radial basis functions）结合，用于插值三维非稳态流问题的解。<ref>{{citation | last1=Walton | first1=S. | last2=Hassan | first2=O. | last3=Morgan | first3=K. | date=2013 |url=https://linkinghub.elsevier.com/retrieve/pii/S0307904X13002771| title="Reduced order modelling for unsteady fluid flow using proper orthogonal decomposition and radial basis functions" | journal=Applied Mathematical Modelling | volume=37 | issue=20–21 | pages=8930–8945 | doi=10.1016/j.apm.2013.04.025}}</ref>

值得一提的是，科学家们已经利用SVD改进了地面引力波干涉仪aLIGO的[[引力波形建模]]（gravitational wave modeling）。<ref>{{citation | last1=Setyawati | first1=Y. | last2=Ohme | first2=F. | last3=Khan | first3=S. | date=2019 | title="Enhancing gravitational waveform model through dynamic calibration" | journal=Physical Review D | volume=99 | issue=2 | pages=024010 | doi=10.1103/PhysRevD.99.024010 | arxiv=1810.07060 | bibcode=2019PhRvD..99b4010S}}</ref>SVD有助于提高波形生成的准确性和速度，支持引力波搜索和更新两种不同的波形模型。

值得一提的是，科学家们已经利用SVD改进了地面引力波干涉仪aLIGO的[[引力波形建模]]（gravitational wave modeling）。<ref>{{citation | last1=Setyawati | first1=Y. | last2=Ohme | first2=F. | last3=Khan | first3=S. | date=2019 | title="Enhancing gravitational waveform model through dynamic calibration" | journal=Physical Review D | volume=99 | issue=2 | pages=024010 | doi=10.1103/PhysRevD.99.024010 | arxiv=1810.07060 | bibcode=2019PhRvD..99b4010S | s2cid=118935941}}</ref>SVD有助于提高波形生成的准确性和速度，支持引力波搜索和更新两种不同的波形模型。

[[推荐系统]]（Recommender systems）中，SVD用于预测用户对项目的评分。<ref>{{citation | last1=Sarwar | first1=Badrul | last2=Karypis | first2=George | last3=Konstan | first3=Joseph A. | last4=Riedl | first4=John T. | date=2000 | title="Application of Dimensionality Reduction in Recommender System – A Case Study" | publisher=University of Minnesota}}</ref>为了在商品机器集群上高效计算SVD，研究人员开发了分布式算法。<ref>{{citation | last1=Bosagh Zadeh | first1=Reza | last2=Carlsson | first2=Gunnar | date=2013 | title="Dimension Independent Matrix Square Using MapReduce" | arxiv=1304.1467 | bibcode=2013arXiv1304.1467B}}</ref>

[[推荐系统]]（Recommender systems）中，SVD用于预测用户对项目的评分。<ref>{{citation | last1=Sarwar | first1=Badrul | last2=Karypis | first2=George | last3=Konstan | first3=Joseph A. | last4=Riedl | first4=John T. | date=2000 | title="Application of Dimensionality Reduction in Recommender System – A Case Study" | publisher=University of Minnesota}}</ref>为了在商品机器集群上高效计算SVD，研究人员开发了分布式算法。<ref>{{citation | last1=Bosagh Zadeh | first1=Reza | last2=Carlsson | first2=Gunnar | date=2013 | title="Dimension Independent Matrix Square Using MapReduce" | arxiv=1304.1467 | bibcode=2013arXiv1304.1467B}}</ref>