Publications and communications of Arnst, Maarten

Arnst, M., Louppe, G., Van Hulle, R., Gillet, L., Bureau, F., & Denoël, V. (May 2022). A hybrid stochastic model and its Bayesian identification for infectious disease screening in a university campus with application to massive COVID-19 screening at the University of Liège. Mathematical Biosciences, 347, 108805. doi:10.1016/j.mbs.2022.108805

Denoël, V.* , Bruyère, O.* , Louppe, G., Bureau, F., D'ORIO, V., Fontaine, S., Gillet, L., Guillaume, M., Haubruge, E., Lange, A.-C., Michel, F., Hulle, R. V., Arnst, M., Donneau, A.-F.* , & Saegerman, C.*. (04 March 2022). Decision-based interactive model to determine re-opening conditions of a large university campus in Belgium during the first COVID-19 wave. Archives of Public Health, 80 (1). doi:10.1186/s13690-022-00801-w

Budo, A., Terrapon, V., Hillewaert, K., Arnst, M., Mouriaux, S., Rodriguez, B., & Jules Bartholet. (2021). Application of a Viscous Through-flow Model to a Modern Axial Low-pressure Compressor (GT2021:59926). In Proceedings of the ASME Turbo Expo 2021 (electronique). doi:10.1115/GT2021-59926

Coheur, J., Torres-Herrador, F., Chatelain, P., Mansour, N., Magin, T., & Arnst, M. (2021). Analytical solution for multi-component pyrolysis simulations of thermal protection materials. Journal of Materials Science. doi:10.1007/s10853-020-05727-8

Liegeois, K., Boman, R., Phipps, E. T., Wiesner, T. A., & Arnst, M. (01 September 2020). GMRES with embedded ensemble propagation for the efficient solution of parametric linear systems in uncertainty quantification of computational models. Computer Methods in Applied Mechanics and Engineering, 369. doi:10.1016/j.cma.2020.113188

Bulthuis, K., Pattyn, F., & Arnst, M. (21 July 2020). A Multifidelity Quantile-Based Approach for Confidence Sets of Random Excursion Sets with Application to Ice-Sheet Dynamics. SIAM/ASA Journal on Uncertainty Quantification, 8 (3), 860-890. doi:10.1137/19M1280466

Torres-Herrador, F.* , Coheur, J.* , Panerai, F., Magin, T., Arnst, M., Mansour, N., & Blondeau, J. (2020). Competitive kinetic model for the pyrolysis of the Phenolic Impregnated Carbon Ablator. Aerospace Science and Technology. doi:10.1016/j.ast.2020.105784

Arnst, M. (2020). Elements of stochastic processes 2019-2020: course material.

Bulthuis, K., Pattyn, F., & Arnst, M. (25 June 2019). Estimation of confidence regions for random excursion sets with application to large-scale ice-sheet simulations. Paper presented at UNCECOMP 2019: 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, Hersonissos, Greece.

Liegeois, K., Boman, R., Phipps, E., Mertens, P., Krasikov, Y., & Arnst, M. (25 June 2019). Ensemble propagation for efficient uncertainty quantification: Application to the thermomechanical modeling of a first mirror for the ITER core CXRS diagnostics. Paper presented at UNCECOMP 2019 / 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering.

Coheur, J., Arnst, M., Magin, T., & Chatelain, P. (24 June 2019). Bayesian parameter inference for PICA devolatilization pyrolysis at high heating rates. Paper presented at 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, Hersonissos, Crete, Greece.

Liegeois, K., Boman, R., Phipps, E., & Arnst, M. (25 April 2019). Efficient parametric computations using ensemble propagation for high dimensional finite element models. Paper presented at CÉCI Scientific Meeting.

Bulthuis, K., Arnst, M., Sun, S., & Pattyn, F. (24 April 2019). Uncertainty quantification of the multi-centennial response of the Antarctic Ice Sheet to climate change. The Cryosphere, 13, 1349-1380. doi:10.5194/tc-13-1349-2019

Bulthuis, K., Arnst, M., Sainan, S., & Pattyn, F. (11 March 2019). Uncertainty Quantification of the Multi-centennial Response of the Antarctic Ice Sheet to climate change. Paper presented at SIAM Conference on Mathematical & Computational Issues in the Geosciences (GS19), Houston, United States.

Coheur, J., Magin, T., Arnst, M., & Chatelain, P. (2019). Bayesian parameter inference for PICA devolatilization pyrolysis at high heating rates. In Proceedings of the 10th VKI PhD Symposium.

Liegeois, K., Boman, R., Phipps, E., & Arnst, M. (28 February 2019). Ensemble Propagation for Efficient Uncertainty Quantification of Mechanical Contact Problems. Paper presented at SIAM Conference on Computational Science and Engineering.

Arnst, M. (2019). Elements of stochastic processes 2018-2019: course material.

Arnst, M., Ponthot, J.-P., & Boman, R. (August 2018). Comparison of stochastic and interval methods for uncertainty quantification of metal forming processes. Comptes Rendus Mécanique, 346 (8), 634-646. doi:10.1016/j.crme.2018.06.007

Liegeois, K., Boman, R., Phipps, E., Wiesner, T., & Arnst, M. (17 April 2018). On the Ensemble Propagation for Efficient Uncertainty Quantification of Mechanical Contact Problems. Paper presented at SIAM Conference on Uncertainty Quanti cation 2018.

Bulthuis, K., Arnst, M., Pattyn, F., & Favier, L. (16 April 2018). Stochastic Modeling of Uncertainties in Fast Essential Antarctic Ice Sheet Models. Paper presented at SIAM Conference on Uncertainty Quantification 2018, Garden Grove (Los Angeles), United States - California.

Coheur, J., Magin, T., Chatelain, P., & Arnst, M. (April 2018). Bayesian Inference on Uncertain Kinetic Parameters for the Pyrolysis of Composite Ablators. Paper presented at SIAM Conference on Uncertainty Quantification (UQ18), Los Angeles, United States.

Hoang Truong, V., Wu, L., Golinval, J.-C., Arnst, M., & Noels, L. (April 2018). Stochastic multiscale model of MEMS stiction accounting for high order statistical moments of non-Gaussian contacting surfaces. Journal of Microelectromechanical Systems, 27 (2), 137-155. doi:10.1109/JMEMS.2018.2797133

Coheur, J., Thierry, M., Arnst, M., & Chatelain, P. (2018). A First Step Towards the Bayesian Inference of Uncertain Kinetic Parameters of Pyrolysis Decomposition Laws. In Proceedings of the 9th VKI PhD Symposium.

Arnst, M. (2018). Modeling with partial differential equations 2017-2018: course material. (ULiège - Université de Liège, MATH0024 Modeling with partial differential equations).

Arnst, M. (2018). Elements of stochastic processes 2017-2018: course material. (ULiège - Université de Liège, MATH0488 Elements of stochastic processes).

Bulthuis, K., Arnst, M., & Pattyn, F. (2017). Modelling ice flow for large-scale ice-sheet simulations.

Arnst, M., Abello Álvarez, B., Ponthot, J.-P., & Boman, R. (15 November 2017). Itô-SDE MCMC method for Bayesian characterization of errors associated with data limitations in stochastic expansion methods for uncertainty quantification. Journal of Computational Physics, 349, 59-79. doi:10.1016/j.jcp.2017.08.005

Arnst, M. (August 2017). Guiding model improvement in dynamic substructuring: sensitivity analysis and nonparametric probabilistic modeling approach. Paper presented at ICOSSAR 2017 International Conference on Structural Safety & Reliability.

Liegeois, K., Boman, R., Mertens, P., Panin, A., Phipps, E., & Arnst, M. (19 July 2017). Ensemble propagation for efficient uncertainty quantification on emerging architectures: Application to thermomechanical contact. Poster session presented at Quantification of Uncertainty: Improving Efficiency and Technology, Trieste, Italy.

Hoang Truong, V., Wu, L., Paquay, S., Golinval, J.-C., Arnst, M., & Noels, L. (June 2017). A computational stochastic multiscale methodology for MEMS structures involving adhesive contact. Tribology International, 110, 401-425. doi:10.1016/j.triboint.2016.10.007

Bulthuis, K., Arnst, M., Pattyn, F., & Favier, L. (April 2017). Uncertainty quantification of Antarctic contribution to sea-level rise using the fast Elementary Thermomechanical Ice Sheet (f.ETISh) model. Paper presented at European Geosciences Union General Assembly 2017, Vienna, Austria.

Coheur, J., Arnst, M., Chatelain, P., & Magin, T. (01 March 2017). Uncertainty Quantification of Aerothermal Flow Simulation Through Low-Density Ablative Thermal Protection Materials. Poster session presented at 8th VKI PhD Symposium, Rhode-Saint-Genèse, Belgium.

Arnst, M. (2017). Reliability and stochastic modeling of engineered systems 2017-2018: course material. (ULiège - Université de Liège, MECA0010 Reliability and stochastic modeling of engineered systems).

Arnst, M. (2017). Elements of stochastic processes 2016-2017: course material. (ULiège - Université de Liège, MATH0488 Elements of stochastic processes).

Arnst, M., & Goyal, K. (2017). Sensitivity analysis of parametric uncertainties and modeling errors in computational-mechanics models by using a generalized probabilistic modeling approach. Reliability Engineering and System Safety, 167, 394-405. doi:10.1016/j.ress.2017.06.007

Hoang Truong, V., Wu, L., Paquay, S., Golinval, J.-C., Arnst, M., & Noels, L. (2017). A Stochastic Multi-scale Model For Predicting MEMS Stiction Failure. In L. V. Starman, J. Hay, & N. Karanjgaokar (Eds.), Micro and Nanomechanics, Volume 5: Proceedings of the 2016 Annual Conference on Experimental and Applied Mechanics (Springer International Publishing, pp. 1-8). New York, United States: The Society for Experimental Mechanics, Inc. doi:10.1007/978-3-319-42228-2_1

Bulthuis, K., Arnst, M., Pattyn, F., & Favier, L. (05 September 2016). Instability and abrupt changes in marine ice sheet behaviour. Paper presented at 1st CRITICS Workshop and Summer School on Critical Transitions in Complex Systems, Kulhuse, Denmark.

Arnst, M. (09 June 2016). Sensitivity analysis of parametric uncertainties and modeling errors in generalized probabilistic modeling. Paper presented at ECCOMAS European Congress on Computational Methods in Applied Sciences and Engineering.

Hoang Truong, V., Paquay, S., Golinval, J.-C., Wu, L., Arnst, M., & Noels, L. (2016). A Stochastic Multi-scale Model For Predicting MEMS Stiction Failure. In Proceedings of the SEM XIII International Congress and Exposition on Experimental and Applied Mechanics. (SEMXIII 2016) (pp. 8).

Arnst, M., Liegeois, K., Boman, R., & Ponthot, J.-P. (18 May 2016). Comparison of interval and stochastic methods for uncertainty quantification in metal forming. Paper presented at ICOMP International Conference on COmputational methods in Manufacturing Processes.

Hoang Truong, V., Wu, L., Paquay, S., Golinval, J.-C., Arnst, M., & Noels, L. (2016). A Study Of Dry Stiction Phenomenon In MEMS Using A Computational Stochastic Multi-scale Methodology. In EuroSimE 2016 in Montpellier (pp. 4). IEEE. doi:10.1109/EuroSimE.2016.7463333

Arnst, M. (2016). Elements of stochastic processes 2015-2016: course material. (ULiège - Université de Liège, MATH0488 Elements of stochastic processes).

Nyssen, F., Arnst, M., & Golinval, J.-C. (2015). Experimental Modal Identification of Mistuning in an Academic Blisk and Comparison With The Blades Geometry Variations. Proceedings of the ASME Turbo Expo. doi:10.1115/GT2015-43436

Arnst, M., Abello Álvarez, B., Boman, R., & Ponthot, J.-P. (26 May 2015). Parametric uncertainty quantification in the presence of modeling errors: Bayesian approach and application to metal. Paper presented at UNCECOMP International Conference on Uncertainty Quantification in Computational Sciences and Engineering, Crète, Greece.

Arnst, M. (May 2015). Nonintrusive probabilistic quantification of uncertainties with application to the management of manufacturing tolerances. Paper presented at IE-net Managing, handling, and modeling uncertainty in mechanical design.

Arnst, M. (2015). Elements of stochastic processes 2014-2015: course material. (ULiège - Université de Liège, MATH0488 Elements of stochastic processes).

Lucas, V., Wu, L., Arnst, M., Golinval, J.-C., Paquay, S., & Noels, L. (27 August 2014). Prediction of meso-scale mechanical properties of poly-silicon materials. Paper presented at EMMC14 - European Mechanics of Materials Conference 2014, Gothenburg, Sweden.

Nyssen, F., Arnst, M., & Golinval, J.-C. (2014). Modeling of Uncertainties in Bladed Disks Using a Nonparametric Approach. Proceedings of the ASME IDETC/CIE 2014. doi:10.1115/DETC2014-35025

Nyssen, F., Arnst, M., & Golinval, J.-C. (July 2014). Nonparametric modelling of multi-stage assemblies of mistuned bladed disks. Paper presented at 5th European Conference on Computational Mechanics, Barcelona, Spain.

Hoang Truong, V., Wu, L., Arnst, M., Golinval, J.-C., Muller, R., Voicu, R., & Noels, L. (26 June 2014). A probabilistic model of the adhesive contact forces between rough surfaces in the MEMS stiction context. Paper presented at 6th International Conference on Advanced Computational Methods in Engineering, ACOMEN 2014, Ghent, Belgium.

Lucas, V., Wu, L., Arnst, M., Golinval, J.-C., Paquay, S., Nguyen, V. D., & Noels, L. (2014). Prediction of macroscopic mechanical properties of a polycrystalline microbeam subjected to material uncertainties. In Á. Cunha, E. Caetano, P. Ribeiro, & G. Muller (Eds.), Proceedings of the 9th International Conference on Structural Dynamics, EURODYN 2014 (pp. 2691-2698).

Nyssen, F., Arnst, M., & Golinval, J.-C. (June 2014). Towards a Nonparametric Modelling of Multi-stage Assemblies of Mistuned Bladed Disks. Poster session presented at ASME Turbo Expo 2014, Düsseldorf, Germany.

Dell'Elce, L., Arnst, M., & Kerschen, G. (2014). Probabilistic Assessment of the Lifetime of Low-Earth-Orbit Spacecraft: Uncertainty Characterization. Journal of Guidance Control and Dynamics. doi:10.2514/1.G000148

Arnst, M. (31 March 2014). UQ Benchmark Problems for Multiphysics Modeling. Paper presented at SIAM Conference on Uncertainty Quantification, Savannah, Georgia, United States.

Arnst, M., Ghanem, R., Phipps, E., & Red-Horse, J. (2014). Reduced chaos expansions with random coefficients in reduced-dimensional stochastic modeling of coupled problems. International Journal for Numerical Methods in Engineering, 97, 352-376. doi:10.1002/nme.4595

Arnst, M., Hoang Truong, V., Cerquaglia, M. L., Xhardez, J., & Dell'Elce, L. (2014). Elements of stochastic processes 2013-2014: course material. (ULiège - Université de Liège, MATH0488 Elements of stochastic processes).

Arnst, M., & Ponthot, J.-P. (2014). An overview of nonintrusive characterization, propagation, and sensitivity analysis of uncertainties in computational mechanics. International Journal for Uncertainty Quantification, 4, 387-421. doi:10.1615/Int.J.UncertaintyQuantification.2014006990

Lucas, V., Wu, L., Arnst, M., Golinval, J.-C., Paquay, S., & Noels, L. (December 2013). Probabilistic model for MEMS micro-beam resonance frequency made of polycrystalline linear anisotropic material. Paper presented at 5th Asia Pacific Congress on Computational Mechanics & 4th International Synposium on Computational Mechanics APCOM & ISCM 2013, Singapore, Singapore.

Arnst, M., & Ponthot, J.-P. (05 September 2013). A probabilistic characterization, propagation, and sensitivity analysis of uncertainties in a metal forming application. Paper presented at COMPLAS International Conference on Computational Plasticity, Barcelona, Spain.

Arnst, M. (23 July 2013). UQ benchmark problems for multiphysics modeling. Paper presented at USNCCM United States National Congress on Computational Mechanics, Raleigh, North Carolina, United States.

Phipps, E., Constantine, P., Red-Horse, J., Ghanem, R., Wildey, T., & Arnst, M. (26 February 2013). Stochastic Dimension Reduction of Multi Physics Systems through Measure Transformation. Paper presented at SIAM Conference on Computational Science and Engineering.

Arnst, M., & Dell'Elce, L. (2013). Elements of stochastic processes 2012-2013: course material. (ULiège - Université de Liège, MATH0488 Elements of stochastic processes).

Arnst, M., Soize, C., & Ghanem, R. (2013). Hybrid Sampling/Spectral Method for Solving Stochastic Coupled Problems. SIAM/ASA Journal on Uncertainty Quantification, 1 (1), 218-243. doi:10.1137/120894403

Arnst, M., Ghanem, R., Phipps, E., & Red-Horse, J. (December 2012). Measure transformation and efficient quadrature in reduced-dimensional stochastic modeling of coupled problems. International Journal for Numerical Methods in Engineering, 92, 1044–1080. doi:10.1002/nme.4368

Arnst, M., Ghanem, R., Phipps, E., & Red-Horse, J. (December 2012). Dimension reduction in stochastic modeling of coupled problems. International Journal for Numerical Methods in Engineering, 92, 940–968. doi:10.1002/nme.4364

Arnst, M., Ghanem, R., Phipps, E., & Red-Horse, J. (02 April 2012). Dimension Reduction and Measure Transformation in Stochastic Multiphysics Modeling. Paper presented at SIAM Conference on Uncertainty Quantification.

Phipps, E., Arnst, M., Constantine, P., Ghanem, R., & Wildey, T. (02 April 2012). Stochastic Dimension Reduction Techniques for Uncertainty Quantification of Multiphysics Systems. Paper presented at SIAM Conference on Uncertainty Quantification.

Arnst, M., Ghanem, R., Phipps, E., & Red-Horse, J. (29 September 2011). Dimension Reduction and Measure Transformation in Stochastic Analysis of Coupled Systems. Paper presented at SAMSI Colloquium, Research Triangle Park, United States.

Arnst, M., Ghanem, R., Phipps, E., & Red-Horse, J. (25 July 2011). Dimension Reduction and Measure Transformation in Stochastic Simulations of Coupled Systems. Paper presented at USNCCM United States National Congress on Computational Mechanics, Minneapolis, United States.

Phipps, E., Arnst, M., Red-Horse, J., & Ghanem, R. (18 July 2011). Uncertain Handshaking for Coupled Physics. Paper presented at ICIAM International Congress on Industrial and Applied Mathematics, Vancouver, Canada.

Ghanem, R., Arnst, M., Phipps, E., & Red-Horse, J. (05 July 2011). Random Handshaking and Information Recovery Between Scales and Models. Paper presented at AMS von Neumann Symposium on Multimodel and Multialgorithm Coupling for Multiscale Problems, Snowbird, United States.

Arnst, M., Ghanem, R., Phipps, E., & Red-Horse, J. (31 March 2011). Dimension reduction and measure transformation in stochastic multiphysics modeling. Paper presented at Stochastic Multiscale Workshop, Banff, Canada.

Arnst, M., Ghanem, R., Phipps, E., & Red-Horse, J. (02 March 2011). Coupling Algorithms for Stochastic Multiphysics. Paper presented at SIAM Conference on Computational Science and Engineering, Reno, United States.

Arnst, M., & Ghanem, R. (2011). A variational-inequality approach to stochastic boundary value problems with inequality constraints and its application to contact and elastoplasticity. International Journal for Numerical Methods in Engineering. doi:10.1002/nme.3307

Arnst, M., Ghanem, R., & Masri, S. (October 2010). Maximum entropy approach to the identification of stochastic reduced-order models of nonlinear dynamical systems. Aeronautical Journal, 114 (1160), 637-650. doi:10.1017/S0001924000004115

Arnst, M., Ghanem, R., & Soize, C. (01 May 2010). Identification of Bayesian posteriors for coefficients of chaos expansions. Journal of Computational Physics, 229 (9), 3134-3154. doi:10.1016/j.jcp.2009.12.033

Arnst, M., & Ghanem, R. (October 2009). Probabilistic Electromechanical Modeling of Nanostructures with Random Geometry. Journal of Computational and Theoretical Nanoscience, 6 (10), 2256-2272. doi:10.1166/jctn.2009.1283

Arnst, M., & Ghanem, R. (01 August 2008). Probabilistic equivalence and stochastic model reduction in multiscale analysis. Computer Methods in Applied Mechanics and Engineering, 197 (43-44), 3584-3592. doi:10.1016/j.cma.2008.03.016

Arnst, M., Clouteau, D., & Bonnet, M. (January 2008). Inversion of probabilistic structural models using measured transfer functions. Computer Methods in Applied Mechanics and Engineering, 197 (6-8), 589-608. doi:10.1016/j.cma.2007.08.011

Chebli, H., Othman, R., Clouteau, D., Arnst, M., & Degrande, G. (January 2008). 3D periodic BE–FE model for various transportation structures interacting with soil. Computers and Geotechnics, 35 (1), 22-32. doi:10.1016/j.compgeo.2007.03.008

Arnst, M. (2007). Inversion of probabilistic models of structures using measured transfer functions. Unpublished doctoral thesis, Ecole Centrale Paris, Chatenay-Malabry, France.
Jury: Clouteau, D. (Promotor), Bonnet, M. (Promotor), Le Tallec, P., Andrieux, S., Degrande, G., Ghanem, R., Soize, C., & Tarantola, A.

Degrande, G., Clouteau, D., Othman, R., Arnst, M., Chebli, H., Klein, R., Chatterjee, P., & Janssens, B. (June 2006). A numerical model for ground-borne vibrations from underground railway traffic based on a periodic finite element–boundary element formulation. Journal of Sound and Vibration, 293 (3-5), 645-666. doi:10.1016/j.jsv.2005.12.023

Arnst, M., Clouteau, D., Chebli, H., Othman, R., & Degrande, G. (January 2006). A non-parametric probabilistic model for ground-borne vibrations in buildings. Probabilistic Engineering Mechanics, 21 (1), 18-34. doi:10.1016/j.probengmech.2005.06.004

Clouteau, D., Arnst, M., Al-Hussaini, T., & Degrande, G. (06 May 2005). Freefield vibrations due to dynamic loading on a tunnel embedded in a stratified medium. Journal of Sound and Vibration, 283 (1-2), 173-199. doi:10.1016/j.jsv.2004.04.010