Preprints

[1] Inverse Problems: A Deterministic Approach using Physics-Based Reduced Models [Lecture Notes].
O. Mula. 2022. [pdf]

[2] Deep Learning-Based schemes for Singularly Perturbed Convection-Diffusion Problems.
A. Beguinet, V. Ehrlacher, R. Flenghi, M. Fuente, O. Mula and A. Somacal. 2022. [pdf]

[3] Wasserstein Model Reduction Approach for Parametrized Flow Problems in Porous Media.
B. Battisti, T. Blickham, G. Enchery, V. Ehrlacher, D. Lombardi and O. Mula. 2022. [pdf]

[4] Impact of Physical Model Error on State Estimation for Neutronics Applications.
Y. Conjungo, D. Labeurthre, F. Madiot, O. Mula and T. Taddei. 2022. [pdf]

[5] Depth-Adaptive Neural Networks from the Optimal Control Viewpoint.
J. Aghili and O. Mula. 2020. [pdf, code]

Journal Articles

[A1] State Estimation with Model Reduction and Shape Variability. Application to biomedical problems.
F. Galarce, D. Lombardi and O. Mula. SIAM J. Scientific Computing (in print). 2022. [pdf]

[A2] Nonlinear reduced models for state and parameter estimation.
A. Cohen, W. Dahmen, O. Mula and J. Nichols. SIAM J. Uncertainty Quantification (in print). 2022. [pdf, doi]

[A3] Epidemiological Forecasting with Model Reduction of Compartmental Models. Application to the COVID-19 Pandemic.
A. Bakhta, T. Boiveau, Y. Maday and O. Mula. Biology. 2021. [pdf, doi]

[A4] Fast reconstruction of 3D blood flows from Doppler ultrasound images and reduced models.
F. Galarce, J.F. Gerbeau, D. Lombardi and O. Mula. Computer Methods in Applied Mechanics and Engineering. 2021. [pdf, doi]

[A5] Reconstructing Haemodynamics Quantities of Interest from Doppler Ultrasound Imaging.
F. Galarce, D. Lombardi and O. Mula. Int. J. Numer. Meth. Biomedical Eng. . 2020. [pdf, doi]

[A6] Optimal reduced model algorithms for data-based state estimation.
A. Cohen, W. Dahmen, R. DeVore, J. Fadili, O. Mula and J. Nichols. SIAM Journal on Numerical Analysis. 2020. [pdf, doi]

[A7] An Adaptive Parareal Algorithm.
Y. Maday and O. Mula. Journal of Computational and Applied Mathematics. 2020. [pdf, doi, code]

[A8] Nonlinear model reduction on metric spaces. Application to one-dimensional conservative PDEs in Wasserstein spaces.
V. Ehrlacher, D. Lombardi, O. Mula and F.-X. Vialard. ESAIM M2AN. 2020. [pdf, doi, code]

[A9] An Adaptive Nested Source Term Iteration for Radiative Transfer Equations.
W. Dahmen, F. Gruber and O. Mula. Mathematics of Computation. 2020. [pdf, doi, code]

[A10] Homogenization in the energy variable for a neutron transport model.
H. Hutridurga, O. Mula and F. Salvarani. Asymptotic Analysis. 2019. [pdf, doi]

[A11] Greedy algorithms for optimal measurements selection in state estimation using reduced models.
P. Binev, A. Cohen, O. Mula and J. Nichols. SIAM Journal on Uncertainty Quantification. 2018. [pdf, doi]

[A12] Sensor placement in nuclear reactors based on the Generalized Empirical Interpolation Method.
J.-P. Argaud, B. Bouriquet, F. de Caso, H. Gong, Y. Maday and O. Mula. Journal of Computational Physics. 2018. [pdf, doi]

[A13] The DUNE-DPG library for solving PDEs with Discontinuous Petrov-Galerkin finite elements.
F. Gruber, A. Klewinghaus and O. Mula. Archive of Numerical Software. 2017. [pdf, doi, code]

[A14] Convergence analysis of the Generalized Empirical Interpolation Method.
Y. Maday, O. Mula and G. Turinici. SIAM Journal on Numerical Analysis. 2016. [pdf, doi]

[A15] The Generalized Empirical Interpolation Method: Stability theory on Hilbert spaces with an application to the Stokes equation.
Y. Maday, O. Mula, A.T. Patera and M. Yano. Computer Methods in Applied Mechanics and Engineering. 2015. [pdf, doi]

[A16] A Generalized Empirical Interpolation Method: application of reduced basis techniques to data assimilation.
Y. Maday and O. Mula. Analysis and Numerics of Partial Differential Equations. 2013. [pdf, doi]

Refereed proceedings

[P1] Stabilization of (G)EIM in Presence of Measurement Noise: Application to Nuclear Reactor Physics.
J. P. Argaud, B. Bouriquet, H. Gong, Y. Maday and O. Mula.
In Spectral and High Order Methods for Partial Differential Equations ICOSAHOM 2016. 2017. [pdf, doi]

[P2] The parareal in time algorithm applied to the kinetic neutron diffusion equation.
A.-M. Baudron, J.J. Lautard, Y. Maday and O. Mula.
In Domain Decomposition Methods in Science and Engineering XXI. 2014. [pdf, doi]

[P3] MINARET: Towards a time-dependent neutron transport parallel solver.
A.M. Baudron, J.J. Lautard, Y. Maday and O. Mula.
In SNA+ MC 2013-Joint International Conference on Supercomputing in Nuclear Applications+ Monte Carlo. 2014. [pdf, doi]

[P4] A priori convergence of the Generalized Empirical Interpolation Method.
Y. Maday, O. Mula and G. Turinici.
In 10th International Conference on Sampling Theory and Applications. 2013. [pdf, doi]

[P5] A new methodology for enhanced natural safety GEN-IV SFR core design: application to a carbide-fueled core.
N.E. Stauff, M. Agard, L. Buiron, B. Fontaine, X. Jeanningros, O. Mula, G. Rimpault and M. Zabiego.
In Proceedings of ICAPP 2011. 2011. [pdf]

Popularization

[1] Report 2: Impact of mobility and population density on the Covid-19 outbreak (February-Nov 2020).
J. Atif, B. Cabot, O. Cappé, O. Mula and Pinot. R.. 2020. [pdf]

[2] Report 1: Feedback on mobility during the Covid-19 epidemic (February-May 2020).
J. Atif, O. Cappé, A. Kazakci, Y. Léo, L. Massoulié and O. Mula. 2020. [pdf]

Thesis

[T1] [Habilitation Thesis] Linear and Nonlinear Schemes for Forward and Inverse Problems.
O. Mula. 2021. [pdf]

[T2] [PhD Thesis] Some contributions towards the parallel simulation of time dependent neutron transport and the integration of observed data in real time..
O. Mula. 2014. [pdf]

Unpublished

[1] PBDW method for state estimation: error analysis for noisy data and nonlinear formulation.
H. Gong, Y. Maday, O. Mula and T. Taddei. 2019. [pdf]