B. Düring, C.-B. Schönlieb and M.-T. Wolfram (eds.).
Gradient flows: from theory to application.
ESAIM Proc. Surveys No. 54, EDP Sciences, France, 2016.

V. Grimm, D. McLaren, R. McLachlan, C.-B. Schönlieb, R. Quispel,
Discrete gradient methods for solving variational image regularisation models,
to appear in the Journal of Physics A, 2017.

L. Calatroni, J. C. De Los Reyes, C.-B. Schönlieb,
Infimal convolution of data discrepancies for mixed noise removal,
to appear in SIAM Journal on Imaging Sciences, arXiv:1611.00690, 2016.

M. Benning, G. Gilboa, J. Grah and C.-B. Schönlieb,
Learning Filter Functions in Regularisers by Minimising Quotients,
Scale Space Var. Meth. Comp. Vis. (SSVM), 12 p., 2017. Preprint arXiv:1704.00989

M. Benning, M. Möller, R. Z. Nossek, M. Burger, D. Cremers, G. Gilboa and C.-B. Schönlieb,
Nonlinear Spectral Image Fusion,
Scale Space Var. Meth. Comp. Vis. (SSVM), 12 p., 2017. Preprint arXiv:1703.08001

M. Benning, M. Betcke, M. J. Ehrhardt, C.-B. Schönlieb,
Gradient descent in a generalised Bregman distance framework, 
2016 Geometric Numerical Integration and its Applications Maths Conference, Melbourne, Australia, to appear in MI Lecture Notes series of Kyushu University, arXiv:1612.02506, 2016.

A. Andersson, M. Kovács, and S. Larsson,
Weak error analysis for semilinear stochastic Volterra equations with additive noise
J. Math. Anal. Appl. 437 (2016), 1283-1304.

A. Andersson, R. Kruse, and S. Larsson,
Duality in refined Sobolev-Malliavin spaces and weak approximation of SPDE
Stochastic Partial Differential Equations: Analysis and Computations 4 (2016), 113-149.

A. Andersson and S. Larsson,
Weak convergence for a spatial approximation of the nonlinear stochastic heat equation
Math. Comp. 85 (2016), 1335-1358.

R. Anton, D. Cohen, S. Larsson, and X. Wang,
Full discretisation of semi-linear stochastic wave equations driven by multiplicative noise
SIAM J. Numer. Anal. 54 (2016), 1093-1119.

Bader, P., Iserles, A., Kropielnicka, K., Singh, P.
Efficient methods for time-dependence in semiclassical Schrödinger equations
to appear in Proc. Royal Soc. A (2017).

Burger, M., Papafitsoros, K., Papoutsellis, E. et al.
Infimal convolution regularisation functionals of BV and L^p spaces
J Math Imaging Vis (2016) 55: 343. doi:10.1007/s10851-015-0624-6

Calatroni, L., van Gennip, Y., Schönlieb, CB. et al.
Graph Clustering, Variational Image Segmentation Methods and Hough Transform Scale Detection for Object Measurement in Images.
J Math Imaging Vis (2016). doi:10.1007/s10851-016-0678-0

L. Calatroni, C. Cao, J. C. De Los Reyes, C.-B. Schönlieb, and T. Valkonen,
Bilevel approaches for learning of variational imaging models
to appear in RICAM special issue, 2016

Celledoni, E.; Kometa, B.K.; Verdier, O.
High Order Semi-Lagrangian Methods for the Incompressible Navier–Stokes Equations
Journal of Scientific Computing 2016 ;Volum 66.(1) s. 91-115

Celledoni, E., Eslitzbichler, M., Schmeding, A.
Shape Analysis on Lie Groups with Applications in Computer Animation
J. Geom. Mech. Vol. 8, no. 3 (2016), pp. 273–304

De los Reyes, J.C., Schönlieb, CB. & Valkonen, T.
Bilevel Parameter Learning for Higher-Order Total Variation Regularisation Models
J Math Imaging Vis (2016). doi:10.1007/s10851-016-0662-8

De Los Reyes, J.C., Schönlieb, C.-B., Valkonen, T.
The structure of optimal parameters for image restoration problems
J. Math. Anal. Appl. 434 (2016), no. 1, 464–500.

Høiseth, E.H.; Celledoni, E.
The averaged Lagrangian method
Journal of Computational and Applied Mathematics 2016

Iserles, A.
The joy and pain of skew-symmetry
Found. Comp. Maths DOI 10.1007/s10208-016-9321-0 (2016).

S. Larsson and M. Molteni,
Numerical solution of parabolic problems based on a weak space-time formulation
Comput. Methods Appl. Math. (2016).

S. Larsson and M. Molteni,
A weak space-time formulation for the linear stochastic heat equation
Int. J. Appl. Comput. Math. (2016).

Lee, J., Cai, X., Lellmann, J., Dalponte, M., Malhi, Y., Butt, N., Morecroft, M., et al.
Individual tree species classification from airborne multi-sensor imagery using robust PCA
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, (2016) 9 (6), 2554-2567.

Marthinsen, H., Owren, B.
Geometric integration of non-autonomous linear Hamiltonian problems
Adv. Comput. Math. 42 (2016), no. 2, 313–332.

Verdier, O., Xue, H., Zanna, A.
A classification of volume preserving generating forms in R^3
Discrete and Continuous Dynamical Systems Vol. 36, no. 4, (2016) pp. 2285-2303