Arnst, M., & Tomasetti, R. (07 March 2024). Design Patterns and Performance Analysis of Polymorphism in Multiphysics FE Assembly on GPU [Paper presentation]. SIAM Conference on Parallel Processing for Scientific Computing (PP24), Baltimore, United States. |
Tomasetti, R., & Arnst, M. (07 March 2024). Efficiently implementing FE boundary conditions using stream-orchestrated execution on GPU [Paper presentation]. SIAM Conference on Parallel Computing for Scientific Computing (PP24), Baltimore, United States. |
Gregov, T., Pattyn, F., & Arnst, M. (10 November 2023). Grounding-line flux conditions for marine ice-sheet systems under effective-pressure-dependent and hybrid friction laws. Journal of Fluid Mechanics, 975. doi:10.1017/jfm.2023.760 |
Delhez, C., Rivière, N., Erpicum, S., Pirotton, M., Archambeau, P., Arnst, M., Bierens, J., & Dewals, B. (2023). Drift of a drowning victim in rivers: conceptualization and global sensitivity analysis under idealized flow conditions. Water Resources Research. doi:10.1029/2022WR034358 |
Budo, A., Hillewaert, K., Arnst, M., Le Men, T., & Terrapon, V. (2023). Quantification of geometric variability effects through a viscous through-flow model: sensitivity analysis of the manufacturing tolerance effects on performance of modern axial-flow compressor blades. In Proceedings of the ASME Turbo Expo. ASME. doi:10.1115/GT2023-102800 |
Schmitz, V., Arnst, M., El kadi Abderrezzak, K., Pirotton, M., Erpicum, S., Archambeau, P., & Dewals, B. (2023). Global sensitivity analysis of a dam breaching model: To which extent is parameter sensitivity case-dependent? Water Resources Research. doi:10.1029/2022WR033894 |
Gregov, T., Pattyn, F., & Arnst, M. (24 April 2023). Investigation of numerical continuation methods for marine ice-sheet systems formulated as contact problems [Poster presentation]. EGU General Assembly 2023, Vienne, Austria. doi:10.5194/egusphere-egu23-8932 |
Gregov, T., Pattyn, F., & Arnst, M. (2023). Extension of flux conditions for marine ice-sheet systems. |
Coheur, J., Magin, T., Chatelain, P., & Arnst, M. (2023). Bayesian identification of pyrolysis model parameters for thermal protection materials using an adaptive gradient-informed sampling algorithm with application to a Mars atmospheric entry. International Journal for Uncertainty Quantification, 13 (2), 53-80. doi:10.1615/int.j.uncertaintyquantification.2022042928 |
Gregov, T., Pattyn, F., & Arnst, M. (2022). Derivation of grounding-line flux conditions for marine ice-sheet systems. |
Gregov, T., Pattyn, F., & Arnst, M. (01 September 2022). Numerical continuation methods for marine ice-sheet systems with various friction laws [Paper presentation]. ACOMEN 2022, Liège, Belgium. |
Budo, A., Bartholet Jules, Hillewaert, K., Arnst, M., & Terrapon, V. (02 August 2022). Geometrical variability in a through-flow model: manufacturing tolerance effects on compressor blades [Paper presentation]. ACOMEN 2022. |
Gregov, T., Pattyn, F., & Arnst, M. (09 June 2022). A primal-dual formulation for numerical simulations of marine ice sheets with various friction laws [Paper presentation]. ECCOMAS Congress 2022, Oslo, Norway. |
Gregov, T., Pattyn, F., & Arnst, M. (24 May 2022). Extension of marine ice-sheet flux conditions to effective-pressure-dependent and hybrid friction laws [Paper presentation]. EGU General Assembly 2022, Vienne, Austria. doi:10.5194/egusphere-egu22-10208 |
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 * These authors have contributed equally to this work. |
Schmitz, V., Arnst, M., El Kadi Abderrezzak, K., Pirotton, M., Erpicum, S., Archambeau, P., & Dewals, B. (2022). Uncertainty analysis of a lumped physically based numerical model of dam breaching. In Proceedings of the 39th IAHR World Congress. International Association for Hydro-Environment Engineering and Research (IAHR). doi:10.3850/IAHR-39WC2521716X2022466 |
Budo, A., Mouriaux Sophie, Bartholet Jules, Arnst, M., & Terrapon, V. (14 October 2021). Geometric uncertainties in through-flow model [Paper presentation]. Journées des doctorants Safran (JDD HAIDA). |
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). ASME. 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 |
Arnst, M., Soize, C., & Bulthuis, K. (2021). Computation of sobol indices in global sensitivity analysis from small data sets by probabilistic learning on manifolds. International Journal for Uncertainty Quantification, 11 (2), 1 - 23. doi:10.1615/Int.J.UncertaintyQuantification.2020032674 |
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 * These authors have contributed equally to this work. |
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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. COMPLAS International Conference on Computational Plasticity, Barcelona, Spain. |
Arnst, M. (23 July 2013). UQ benchmark problems for multiphysics modeling [Paper presentation]. 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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. 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 presentation]. Stochastic Multiscale Workshop, Banff, Canada. |
Arnst, M., Ghanem, R., Phipps, E., & Red-Horse, J. (02 March 2011). Coupling Algorithms for Stochastic Multiphysics [Paper presentation]. 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 [Doctoral thesis, Ecole Centrale Paris]. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/103160 |
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 |