Background and activities
Elena Celledoni has been employed at the Department of Mathematical Sciences since 2004. She is a professor of mathematics since 2009. She is a member of the group of differential equations and numerical analysis.
She received her Master degree in mathematics from the University of Trieste in 1993, and her Ph.D in computational mathematics from the University of Padua, Italy, 1997. She held post doc positions at the University of Cambridge, UK, at the
Mathematical Sciences Research Institute, Berkeley, California and at NTNU.
Her research field is in numerical analysis and in particular structure preserving algorithms for differential equations and geometric numerical integration.
Memberships, leadership experience (selection)
- Leader of the Differential Equations and Numerical Analysis Group at the Department of Mathematical Sciences and NTNU
- Member of the European Consortium of Mathematics in Industry Council
- Member of the board of the International Council of Mathematics in Industry and Applications
- Member of the Royal Norwegian Society of Sciences and Letters.
Co-organiser of a special semester at Isaac Newton Institute of MS, 2019. Geometry, compatibility and structure preservation
Member of the editorial board of SIAM Review, JCD, JGM, Calcolo, Networks and Heterogeneous Media
- TMA4205 - Numerical Linear Algebra
- MA8004 - Mathematical Sciences Seminar for PhD-students - mini
- MA8001 - Mathematical Sciences Seminar for PhD-students
- MA2501 - Numerical Methods
- TMA4212 - Numerical Solution of Differential Equations by Difference Methods
Scientific, academic and artistic work
A selection of recent journal publications, artistic productions, books, including book and report excerpts. See all publications in the database
- (2022) Computational geometric methods for preferential clustering of particle suspensions. Journal of Computational Physics. vol. 448.
- (2021) An integral model based on slender body theory, with applications to curved rigid fibers. Physics of Fluids. vol. 33 (4).
- (2021) Structure preserving deep learning. European journal of applied mathematics.
- (2021) Equivariant neural networks for inverse problems. Inverse Problems. vol. 37 (8).
- (2021) Discrete conservation laws for finite element discretisations of multisymplectic PDEs. Journal of Computational Physics. vol. 444.
- (2021) Lie Group integrators for mechanical systems. International Journal of Computer Mathematics.
- (2019) Deep learning as optimal control problems: models and numerical methods. Journal of Computational Dynamics. vol. 6 (2).
- (2019) Energy-preserving methods on Riemannian manifolds. Mathematics of Computation. vol. 89 (322).
- (2019) Discrete Darboux polynomials and the search for preserved measures and integrals of rational maps. arXiv.org.
- (2019) Using discrete Darboux polynomials to detect and determine preserved measures and integrals of rational maps. Journal of Physics A: Mathematical and Theoretical. vol. 52 (31).
- (2019) Using discrete Darboux polynomials to detect and determine preserved measures and integrals of rational maps. arXiv.org.
- (2019) Energy-Preserving Integrators Applied to Nonholonomic Systems. Journal of nonlinear science. vol. 29 (4).
- (2019) Predicting bending moments with machine learning. Lecture Notes in Computer Science (LNCS). vol. LNCS11712.
- (2019) Signatures in Shape analysis: An efficient approach to motion identification. Lecture Notes in Computer Science (LNCS). vol. 11712 LNCS.
- (2019) Geometric and integrability properties of Kahan?s method: The preservation of certain quadratic integrals. Journal of Physics A: Mathematical and Theoretical. vol. 52 (6).
- (2019) Krylov projection methods for linear Hamiltonian systems. Numerical Algorithms.
- (2019) A novel approach to rigid spheroid models in viscous flows using operator splitting methods. Numerical Algorithms. vol. 81 (4).
- (2019) Computational methods for tracking inertial particles in discrete incompressible flows. arXiv.org.
- (2019) Three classes of quadratic vector fields for which the Kahan discretization is the root of a generalised Manin transformation. Journal of Physics A: Mathematical and Theoretical. vol. 52 (4).
- (2018) Dissipative numerical schemes on Riemannian manifolds with applications to gradient flows. SIAM Journal on Scientific Computing. vol. 40 (6).