Publications

[1]

M. A. Iglesias, Michael. E. Causon, M. Y. Matveev, A. Endruweit, and M. V. Tretyakov, “DeepONet-accelerated Bayesian inversion for moving boundary problems.” 2025. Available: https://arxiv.org/abs/2512.20268

[2]

M. E. Causon, M. A. Iglesias, M. Y. Matveev, A. Endruweit, and M. V. Tretyakov, “Real-time Bayesian inversion in resin transfer moulding using neural surrogates,” Composites Part A: Applied Science and Manufacturing, vol. 185, p. 108355, 2024, doi: https://doi.org/10.1016/j.compositesa.2024.108355.

[3]

M. Iglesias, X. Li, M. Sovetova, and Y. Wu, “Bayesian inversion for in-situ thermal characterisation of walls in the presence of thermal anomalies,” Energy and Buildings, vol. 319, p. 114558, 2024, doi: https://doi.org/10.1016/j.enbuild.2024.114558.

[4]

C.-H. M. Tso, M. Iglesias, and A. Binley, “Ensemble kalman inversion of induced polarization data,” Geophysical Journal International, vol. 236, no. 3, pp. 1877–1900, Jan. 2024, doi: 10.1093/gji/ggae012.

[5]

N. K. Chada, M. Iglesias, S. Lu, and F. Werner, “On a dynamic variant of the iteratively regularized gauss–newton method with sequential data,” SIAM Journal on Scientific Computing, vol. 45, no. 6, pp. A3020–A3046, 2023, doi: 10.1137/22M1512442.

[6]

M. Iglesias, D. M. McGrath, M. V. Tretyakov, and S. T. Francis, “Ensemble kalman inversion for magnetic resonance elastography,” Physics in Medicine & Biology, vol. 67, no. 23, p. 235003, Nov. 2022, doi: 10.1088/1361-6560/ac9fa1.

[7]

M. Y. Matveev, A. Endruweit, A. C. Long, M. A. Iglesias, and M. V. Tretyakov, “Bayesian inversion algorithm for estimating local variations in permeability and porosity of reinforcements using experimental data,” Composites Part A: Applied Science and Manufacturing, vol. 143, p. 106323, 2021, doi: https://doi.org/10.1016/j.compositesa.2021.106323.

[8]

C.-H. M. Tso, M. Iglesias, P. Wilkinson, O. Kuras, J. Chambers, and A. Binley, “Efficient multiscale imaging of subsurface resistivity with uncertainty quantification using ensemble kalman inversion,” Geophysical Journal International, vol. 225, no. 2, pp. 887–905, Jan. 2021, doi: 10.1093/gji/ggab013.

[9]

M. Iglesias and Y. Yang, “Adaptive regularisation for ensemble kalman inversion,” Inverse Problems, vol. 37, no. 2, p. 025008, Jan. 2021, doi: 10.1088/1361-6420/abd29b.

[10]

P. Wate, M. Iglesias, V. Coors, and D. Robinson, “Framework for emulation and uncertainty quantification of a stochastic building performance simulator,” Applied Energy, vol. 258, p. 113759, 2020, doi: https://doi.org/10.1016/j.apenergy.2019.113759.

[11]

S. Ruchi, S. Dubinkina, and M. A. Iglesias, “Transform-based particle filtering for elliptic bayesian inverse problems,” Inverse Problems, vol. 35, no. 11, p. 115005, Oct. 2019, doi: 10.1088/1361-6420/ab30f3.

[12]

P. Wate, V. Coors, M. Iglesias, and D. Robinson, “Uncertainty assessment of building performance simulation: An insight into suitability of methods and their applications,” in Urban energy systems for low-carbon cities, U. Eicker, Ed., Academic Press, 2019, pp. 257–287. doi: https://doi.org/10.1016/B978-0-12-811553-4.00007-X.

[13]

L. De Simon, M. Iglesias, B. Jones, and C. Wood, “Quantifying uncertainty in thermophysical properties of walls by means of bayesian inversion,” Energy and Buildings, vol. 177, pp. 220–245, 2018, doi: https://doi.org/10.1016/j.enbuild.2018.06.045.

[14]

M. Iglesias, M. Park, and M. V. Tretyakov, “Bayesian inversion in resin transfer molding,” Inverse Problems, vol. 34, no. 10, p. 105002, Jul. 2018, doi: 10.1088/1361-6420/aad1cc.

[15]

M. Iglesias, Z. Sawlan, M. Scavino, R. Tempone, and C. Wood, “Ensemble-marginalized kalman filter for linear time-dependent PDEs with noisy boundary conditions: Application to heat transfer in building walls,” Inverse Problems, vol. 34, no. 7, p. 075008, May 2018, doi: 10.1088/1361-6420/aac224.

[16]

N. K. Chada, M. A. Iglesias, L. Roininen, and A. M. Stuart, “Parameterizations for ensemble kalman inversion,” Inverse Problems, vol. 34, no. 5, p. 055009, Apr. 2018, doi: 10.1088/1361-6420/aab6d9.

[17]

M. Iglesias, Z. Sawlan, M. Scavino, R. Tempone, and C. Wood, “Bayesian inferences of the thermal properties of a wall using temperature and heat flux measurements,” International Journal of Heat and Mass Transfer, vol. 116, pp. 417–431, 2018, doi: https://doi.org/10.1016/j.ijheatmasstransfer.2017.09.022.

[18]

M. M. Dunlop, M. A. Iglesias, and A. M. Stuart, “Hierarchical bayesian level set inversion,” Statistics and Computing, vol. 27, no. 6, pp. 1555–1584, 2017, doi: 10.1007/s11222-016-9704-8.

[19]

M. A. Iglesias, K. Lin, S. Lu, and A. M. Stuart, “Filter based methods for statistical linear inverse problems,” Communications in Math. Sciences, vol. 15, no. 7, pp. 1867–1896., 2017.

[20]

P. Wate, V. Coors, D. Robinson, and M. Iglesias, “QUALITATIVE SCREENING METHOD FOR IMPACT ASSESSMENT OF UNCERTAIN BUILDING GEOMETRY ON THERMAL ENERGY DEMAND PREDICTIONS,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII–2/W2, pp. 127–134, 2016, doi: 10.5194/isprs-archives-XLII-2-W2-127-2016.

[21]

M. A. Iglesias, Y. Lu, and A. Stuart, “A Bayesian level set method for geometric inverse problems,” Interfaces Free Boundries, vol. 18, pp. 181–217, 2016.

[22]

M. A. Iglesias, “A regularizing iterative ensemble Kalman method for PDE-constrained inverse problems,” Inverse Problems, vol. 32, no. 2, p. 025002, 2016, Available: http://stacks.iop.org/0266-5611/32/i=2/a=025002

[23]

M. A. Iglesias, K. Lin, and A. M. Stuart, “Well-posed Bayesian geometric inverse problems arising in subsurface flow,” Inverse Problems, vol. 30, p. 114001, 2014, doi: doi:10.1088/0266-5611/30/11/114001.

[24]

M. A. Iglesias, “Iterative regularization for ensemble data assimilation in reservoir models,” Computational Geosciences, vol. 19, no. 1, pp. 177–212, 2014.

[25]

M. A. Iglesias, K. J. H. Law, and A. M. Stuart, “Ensemble kalman methods for inverse problems,” Inverse Problems, vol. 29, no. 4, p. 045001, Mar. 2013, doi: 10.1088/0266-5611/29/4/045001.

[26]

M. A. Iglesias, “A regularizing iterative ensemble kalman method for PDE-constrained inverse problems,” Inverse Problems, vol. 32, no. 2, p. 025002, Jan. 2016, doi: 10.1088/0266-5611/32/2/025002.

[27]

M. A. Iglesias, K. J. H. Law, and A. M. Stuart, “Evaluation of Gaussian approximations for data assimilation in reservoir models,” Computational Geosciences, vol. 17, no. 5, pp. 851–885, 2013, doi: 10.1007/s10596-013-9359-x.

[28]

M. A. Iglesias and D. McLaughlin, “Data inversion in coupled subsurface flow and geomechanics models,” Inverse Problems, vol. 28, p. 115009, 2012.

[29]

M. A. Iglesias and D. McLaughlin, “Level-set techniques for facies identification in reservoir modeling,” Inverse Problems, vol. 27, p. 035008, 2011.

[30]

M. A. Iglesias and C. Dawson, “An iterative representer-based scheme for data inversion in reservoir modeling,” Inverse Problems, vol. 25, p. 035006, 2009.

[31]

M. A. Iglesias and C. Dawson, “The representer method for state and parameter estimation in single-phase Darcy flow,” Comp. Meth. Appl. Mech. Eng., vol. 196, pp. 4577–4596, 2007.