Research Output

Research never rests, and never stops. A list of our publications are below, showcasing our work and results over the past years including journals, conference papers and presentations.

  1. Absil CO, Filippi G, Riccardi A, Vasile M. A Variance-Based Estimation of the Resilience Indices in the Preliminary Design Optimisation of Engineering Systems Under Epistemic Uncertainty. EUROGEN, Madrid: 2017.
  2. Vasile M, Filippi G, Ortega Absil C, Riccardi A. Fast belief estimation in evidence network models. EUROGEN, Madrid: 2017.
  3. Filippi G, Vasile M, Korondi PZ, Marchi M, Poloni C. Robust design optimisation of dynamical space systems. 8th Int. Syst. Concurr. Eng. Sp. Appl. Conf., Glasgow: 2018.
  4. Filippi G, Marchi M, Vasile M, Vercesi P. Evidence-Based Robust Optimisation of Space Systems with Evidence Network Models. IEEE Congr. Evol. Comput. CEC, Rio De Janeiro: 2018.
  5. Filippi G, Krpelik D, Korondi PZ, Vasile M, Marchi M, Poloni C. Space systems resilience engineering and global system reliability optimisation under imprecision and epistemic uncertainty. Proc. Int. Astronautica Congr. IAC, Bremen: 2018.
  6. Filippi G, Vasile M. A Memetic Approach to the Solution of Constrained Min-Max Problems. 2019 IEEE Congr. Evol. Comput. CEC, Wellington: 2019.
  7. Filippi G, Vasile M. A Multi Layer Evidence Network Model for the Design Process of Space Systems under Epistemic Uncertainty. EUROGEN, Guimaraes: 2019.
  8. Filippi G, Vasile M. Evidence-based resilience engineering of dynamic space systems. Proc. Int. Astronaut. Congr. IAC, Washington: 2019.
  9. Filippi G, Vasile M, Krpelik D, Korondi PZ, Marchi M, Poloni C. Space systems resilience optimisation under epistemic uncertainty. Acta Astronaut 2019. doi: 10.1016/j.actaastro.2019.08.024.
  10. Greco C, Gentile L, Filippi G, Minisci E, Vasile M, Bartz-Beielstein T. Autonomous Generation of Observation Schedules for Tracking Satellites with Structured-Chromosome GA Optimisation. IEEE Congr. Evol. Comput. CEC, Wellington: 2019.
  11. Gentile L, Filippi G, Minisci E, Bartz-Beielstein T, Vasile M. Preliminary spacecraft design by means of Structured-Chromosome Genetic Algorithms. IEEE Congr. Evol. Comput., Glasogw: 2020.
  12. Filippi G, Gillespie D, Ross Wilson A, Vasile M. A resilience approach to the design of future Moon base power systems. Int. Astronaut. Congr. IAC: 2020.
  13. Gillespie D, Ross Wilson A, Martin D, Mitchell G, Filippi G, Vasile M. Comparative analysis of Solar power satellite systems to support a Moon base. Int. Astronaut. Congr. IAC: 2020.
  14. Filippi G, Vasile M. Inflationary Differential Evolution for Constrained Multi-Objective Optimisation Problems. International Conference on Bio-inspired Optimisation Methods and Their Application BIOMA, Bruxelles: 2020.
  15. Filippi G, Vasile M. Network resilience optimisation of complex systems. International Conference on Uncertainty Quantification and Optimisation UQOP, Bruxelles: 2020.
  16. Filippi G, Vasile M. Global Solution of Constrained Min-Max Problems with Inflationary Differential Evolution. In: Minisci E, Riccardi A, Vasile M, editors. Optim. Sp. Eng. OSE, Springer; 2020.
  17. Filippi G, Vasile M. Introduction to Evidence-Based Robust Optimisation. In: Vasile M, editor. Optim. Under Uncertain. with Appl. to Aerosp. Eng., Springer Nature; 2020.
  18. Filippi G, Vasile M. A Multi Layer Evidence Network Model for the Design Process of Space Systems under Epistemic Uncertainty. In: Computational Methods in Applied Sciences, Springer ECCOMAS; 2020.
  19. Greco, C., Campagnola, S., & Vasile, M. (2020, under review). Robust Space Trajectory Design using Belief Optimal Control. Journal of Guidance, Control, and Dynamics.
  20. Greco, C., & Vasile, M. (2020, October). Closing the Loop between Mission Design and Navigation Analysis. In 71th International Astronautical Congress, The Cyberspace Edition.
  21. Acciarini, G., Greco, C., & Vasile, M. (2020, August). On the solution of the Fokker-Planck equation without diffusion for uncertainty propagation in orbital dynamics. In 2020 AAS/AIAA Astrodynamics Specialist Conference, South Lake Tahoe, CA, US.
  22. Walker, L., Di Carlo, M., Greco, C., Vasile, M., & Warden, M. (2020, under review). A Mission Concept for the Low-Cost Large-Scale Exploration and Characterisation of NEOs. Advances in Space Research.
  23. Greco, C., & Vasile, M. (2020, in press). Fundamentals of Filtering. In Optimization Under Uncertainty with Applications to Aerospace Engineering. Vasile, M. (editor). Springer Nature.
  24. Rutledge, N., Kershaw, A., Hashim, A., Greco, C., Riccardi, A., Minisci, E., & Akartunali, K. (2020, in press). Introduction to Optimisation. In Optimization Under Uncertainty with Applications to Aerospace Engineering. Vasile, M. (editor). Springer Nature.
  25. Greco, C., Di Carlo, M., Vasile, M., & Epenoy, R. (2020). Direct multiple shooting transcription with polynomial algebra for optimal control problems under uncertainty. Acta Astronautica, 1 70, 224-234.
  26. Greco, C., Campagnola, S., & Vasile, M. (2020). Robust space trajectory design using belief stochastic optimal control. In AIAA Scitech 2020 Forum, Orlando, FL, US (p. 1471).
  27. Gentile, L., Greco, C., Minisci, E., Bartz-Beielstein, T., & Vasile, M. (2019, October). An optimization approach for designing optimal tracking campaigns for low-resources deep- space missions. In 70th International Astronautical Congress, Washington, D.C., US.
  28. Gentile, L., Greco, C., Minisci, E., Bartz-Beielstein, T., & Vasile, M. (2019, July). Structured- chromosome GA optimization for satellite tracking. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO), Prague, Czech Republic (pp. 1955-1963).
  29. Greco, C., Gentile, L., Vasile, M., Minisci, E., & Bartz-Beielstein, T. (2019, August). Robust particle filter for space objects tracking under severe uncertainty. In 2019 AAS/AIAA Astrodynamics Specialist Conference, Portland, ME, US.
  30. Walker, L., Greco, C., Di Carlo, M., Wilson, A. R., Ricciardi, L., Berquand, A., & Vasile, M. (2019). Nanospacecraft Exploration of Asteroids by Collision and flyby Reconnaissance (NEACORE). In 13th IAA Low Cost Planetary Mission Conference, Toulouse, France.
  31. Di Carlo, M., Vasile, M., Greco, C., & Epenoy, R. (2019, February). Robust optimization of low-thrust interplanetary transfers using evidence theory. In 29th AAS/AIAA Space Flight Mechanics Meeting, Ka’anapali, HI, United States (pp. 339-358).
  32. Greco, C., Di Carlo, M., Vasile, M., & Epenoy, R. (2018, October). An intrusive polynomial algebra multiple shooting approach to the solution of optimal control problems. In 69th International Astronautical Congress, Bremen, Germany.
  33. Greco, C., Di Carlo, M., Walker, L., & Vasile, M. (2018, May). Analysis of NEOs reachability with nano-satallites and low-thrust propulsion. In 4S Symposium 2018-Small Satellites Systems and Services, Sorrento, Italy.
  34. Antoniou, G. Petelin, G. Papa, On Formulating the Ground Scheduling Problem as a Multi-objective Bilevel Problem, BIOMA, Brussels, Belgium, 17-20 November 2020 (accepted)
  35. Irawan, M. Antoniou, B. Naujoks, G. Papa, Refining the CC-RDG3 Algorithm with Increasing Population Scheme and Persistent Covariance Matrix, BIOMA, Brussels, Belgium, 17-20 November 2020 (accepted)
  36. Antoniou, G. Papa, Solving min-max optimisation problems by means of bilevel evolutionary algorithms: a preliminary study, Proc. Genetic and Evolutionary Computation Conference Companion, Cancun, Mexico: 8-12 July 2020 DOI: 10.1145/3377929.3390037
  37. Margarita Antoniou , Peter Korošec, Gregor Papa , An adaptive evolutionary surrogate-based approach for single-objective bilevel optimisation, UQOP, Paris, France, 2019
  38. Margarita Antoniou, Thomas Krak, Alexander Erreygers , Jasper De Bock, Bounding limit expectations of Markov chains using evolutionary algorithms, Gert de Cooman, OSE, Ljubljana, Slovenia, November 2019
  39. Margarita Antoniou , Peter Korošec , Gregor Papa, Parameter control in evolutionary bilevel optimisation, IPSSC, Planica, Slovenia, April 2019
  40. Margarita Antoniou, Evolutionary approaches for bilevel optimisation, AVN, Ljubljana, Slovenia, 2018
  41. Petelin, M. Antoniou, G. Papa, Multi-objective approaches to ground station scheduling for optimization of communication with satellites, Optimization and Engineering Special Issue, Springer (2020) (under review)
  42. Antoniou, M., Hribar, R., Papa, G., Parameter control in evolutionary optimisation,.: Springer Nature (2020) (under publication)
  43. Antoniou, M.,Korošec P., Multilevel optimisation,: Springer Nature (2020) (under publication)
  44. Papa G., Antoniou M., Vukašinović V., Dejavnosti v okviruprojekta H2020 MSCA UTOPIAE. Novice IJS, ISSN 1581-2707. [Tiskanaizd.], mar. 2020, št. 192, str. 12-13. [COBISS.SI-ID 15845379]
  45. Dani Irawan, Boris Naujoks, Comparison of Reference- and Hypervolume-Based MOEA on Solving Many-Objective Optimization Problems. (EMO2019) https://doi.org/10.1007/978-3-030-12598-1_22
  46. Dani Irawan, Boris Naujoks, Michael Emmerich, Cooperative-Coevolution-CMA-ES with Two-Stage Grouping. (CEC2020) https://doi.org/10.1109/CEC48606.2020.9185616
  47. Thomas Krak: Computing Expected Hitting Times for Imprecise Markov Chains, accepted for publication in Proceedings of UQOP 2020.
  48. Natan T’Joens, Thomas Krak, Jasper De Bock, and Gert de Cooman: A Recursive Algorithm for Computing Inferences in Imprecise Markov Chains, in Proceedings of ECSQARU 2019, pp. 455–465, 2019.
  49. Thomas Krak, Natan T’Joens, and Jasper De Bock: Hitting Times and Probabilities for Imprecise Markov Chains, in Proceedings of ISIPTA 2019, pp. 265–275, 2019.
  50. Matthias Troffaes, Thomas Krak, and Henna Bains: Two-State Imprecise Markov Chains for Statistical Modelling of Two-State Non-Markovian Processes, in Proceedings of ISIPTA 2019, pp. 394–403, 2019.
  51. Thomas Krak, Alexander Erreygers, and Jasper De Bock: An Imprecise Probabilistic Estimator for the Transition Rate Matrix of a Continuous-Time Markov Chain, in Proceedings of SMPS 2018, pp.124–132, 2018.
  52. Thomas Krak, Jasper De Bock, and Arno Siebes: Efficient Computation of Updated Lower Expectations for Imprecise Continuous-Time Hidden Markov Chains, in Proceedings of ISIPTA 2017, pp. 193–204, 2017.
  53. Elisa Morales Tirado, Domenico Quagliarella, and Renato Tognaccini. Airfoil optimization using far-field analysis of the drag force. In AIAA Scitech 2019 Forum, page 0972, 2019.
  54. Lorenzo Gentile, Elisa Morales, Domenico Quagliarella, Edmondo Minisci, Thomas Bartz- Beielstein, and Renato Tognaccini. High-lift devices topology optimisation using structured-chromosome genetic algorithm. In 2020 IEEE Congress on Evolutionary Computation (CEC), pages 1–9. IEEE, 2020.
  55. Domenico Quagliarella, Elisa Morales Tirado, and Andrea Bornaccioni. Risk measures applied to robust aerodynamic shape design optimization. In Flexible Engineering Toward Green Aircraft,pages 153–168. Springer, 2020.
  56. Sabater, P. Congedo, O. Le Maitre, S. Görtz. “A Bayesian Approach for Quantile Optimization Problems with High-Dimensional Uncertainty Sources”. CMAME Journal. (Submitted)
  57. Sabater, P. Bekemeyer, S. Görtz. “Efficient Bi-Level Surrogate Approach for Optimization under Uncertainty of Shock Control Bumps”, AIAA Journal (Accepted for publication)
  58. Sabater, S. Görtz. “Robustness Enhancement of Transonic Aircraft through the Optimization under Uncertainty of Shock Control Bumps”, ICAS 2021: (Accepted for conference)
  59. Sabater,  P. Bekemeyer S. Görtz. “Robust Design of Transonic Natural Laminar Flow Wings under Environmental and Operational Uncertainties”, AIAA SciTech 2021 – Virtual (Accepted for publication)
  60. Sabater “Optimization under Uncertainty of Shock Control Bumps for Transonic Wings”, UQOP 2020. (Accepted for conference)
  61. Sabater, S. Görtz. An Efficient Bi-Level Surrogate Approach for Optimization under Uncertainty of Shock Control Bumps”, AIAA Sci Tech 2019, San Diego
  62. Sabater, S. Görtz. “Gradient-Based Robust Design using the Adjoint Method and Gaussian Processes”. In “Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences”, edited by Antonio Gaspar-Cunha et. al. Springer 2020 (Accepted for publication)
  63. Sabater, S. Görtz. “Best Practices for Surrogate Based Uncertainty Quantification in Aerodynamics and Application to Robust Shape Optimization”. In “Optimization Under Uncertainty with Applications to Aerospace Engineering”, edited by Massimiliano Vasile. Springer 2020 (Accepted for publication)
  64. Gori,  Non-Ideal Compressible Fluid-Dynamics: Developing a Combined Perspective on Modeling, Numerics and Experiments. 2020. Department of Aerospace Science and Technology, Politecnico di Milano, Italy, PhD thesis, Advisor: Prof. Alberto Matteo Attilio Guardone
  65. Gori, M.Zocca, A.Guardone, O.LeMaître and P.M.Congedo. Bayesian Inference of Thermodynamic Models from Vapor Flow Experiments, Computer & Fluids, Vol. 205, 104550, 2020.
  66. Razaaly, G. Persico, G. Gori and P.M. Congedo, Quantile-Based Roust Optimization of a Supersonic Nozzle for Organic-Rankine Cycle Turbines, Applied Mathematical Modelling, Vol. 82, pp. 802-824, 2020
  67. Gori, M. Zocca, G. Cammi, A. Spinelli, P. M. Congedo and A. Guardone, Accuracy Assessment of the Non- Ideal Computational fluid Dynamics Model for Siloxane MDM from the open-source SU2 suite, European Journal of Mechanics-B/Fluids, Vol. 79, pp. 109-120, 2019.
  68. Gori, N. Razaaly, G. Iaccarino and P. M. Congedo, Structural Uncertainty Estimation of Turbulence Models in Organic Rankine Cycle Applications, proceeding at the ORC2019 conference, Athens, Greece, 2019.
  69. Razaaly, G. Gori, G. Iaccarino, P. M. Congedo, Optimization of an ORC Supersonic Nozzle Under Epistemic Uncertainties due to Turbulence Models, proceeding at the Global Power and Propulsion Society GPPS2019 Conference, Zurich, Switzerland, 2019.
  70. Razaaly, G. Gori, O. Le Maître, G. Iaccarino, P. M. Congedo, Robust Optimization of Turbine Cascade for Organic Rankine Cycles Operating with Siloxane MDM, proceeding of the Summer Program at the Center for Turbulence Research, Stanford University, California, USA, 2018.
  71. Gori, A. Turchi, T. Magin, O. Le Maître and P. M. Congedo. Exploring the Impact of the Initial Temperature Field Uncertainty on the Response of Ablative Materials, proceeding at the International Conference on Flight Vehicles, Aerothermodynamics and Re-Entry Missions and Engineering, Bari, Italy, 2019.
  72. Arizmendi, T. Bellosta, A. del Val, G. Gori, M. O. Prazeres and J. Reis, On Real-Time Management of On- Board Ice Protection Systems by Means of Machine Learning, Proceeding at the AIAA Aviation Forum 2019, Dallas, Texas, USA, 2019.
  73. Gori, D. Vimercati and A. Guardone, A Numerical Investigation of Oblique Shock Waves in Non-Ideal Compressible-fluid Flows, proceeding at the 31st International Symposium on Shock Waves ISSW31, Nagoya, Japan, 2018.
  74. Zocca, G. Gori, O. Le Maître, P. M. Congedo and A. Guardone, A Robust Experiment Design for the Investigation of Non-Ideal Compressible Fluid Flow Effects, proceeding at the 7th European Conference on Computational Fluid Dynamics (ECFD7), Glasgow, United Kingdom, 2018.
  75. Razaaly, G. Persico, G. Gori, P. M Congedo, Robust Optimization of a Supersonic ORC Turbine Cascade: a Quantile-Based Approach, proceeding at the 7th European Conference on Computational Fluid Dynamics (ECFD7), Glasgow, United Kingdom, 2018.
  76. Morales E, Korondi PZ, Quagliarella D, et al. Multi-fidelity Surrogate Assisted Design Optimisation of an Airfoil under Uncertainty using Far-Field Drag Approximation. In: UQOP: International Conference on Uncertainty Quantification Optimisation. 2020 (accepted).
  77. Korondi PZ, Marchi M, Parussini L, Quagliarella D, and Poloni C. Multi-objective design optimisation of an airfoil with geometrical uncertainties leveraging multi-fidelity Gaussian process regression. In: UQOP: International Conference on Uncertainty Quantification Optimisation. 2020 (accepted).
  78. Korondi PZ, Marchi M, and Poloni C. Optimization Under Uncertainty with Applications to Aerospace Engineering: Response Surface Methodolgy. In: Springer, 2020 (accepted for publication). Chap. 12.
  79. Korondi PZ, Marchi M, Parussini L, and Poloni C. Multifidelity design optimisation strategy under uncertainty with limited computational budget. Optimization and Engineering, 2020.
  80. Korondi PZ, Marchi M, Parussini L, and Poloni C. Multifidelity Gaussian Process Regression for Propeller Optimisation Under Uncertainty Poster. In: Presentazione attività dottorato, Università degli studi di Trieste. 2020.
  81. Korondi PZ, Parussini L, Marchi M, and Poloni C. Multifidelity Gaussian Process Regression for Propeller Optimisation Under Uncertainty. In: EUROGEN. 2019.
  82. Korondi PZ, Marchi M, Poloni C, and Parussini L. Recursive Polynomial Chaos Co-Kriging for Reliability-based Design Optimisation. In: UQOP: Uncertainty Quantification Optimization Conference. 2019.
  83. Filippi G, Krpelík D, and Korondi PZ. Searching for an optimal and reliable design under epistemic modelling uncertainty. In: UQOP: Uncertainty Quantification and Optimization Conference. 2019.
  84. Korondi PZ, Parussini L, Marchi M, and Poloni C. Reliability based Design Optimisation Of A Ducted Propeller Through Multi-fidelity Learning. In: UNCECOMP: 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering Proceedings. 2019.
  85. Korondi PZ, Marchi M, Parussini L, and Poloni C. New Approach for the Optimisation of a Ducted Propeller Under Uncertainty. In: NAFEMS European Conference on Simulation-Based Optimisation. 2019.
  86. Rehback, L. Gentile, T. Bartz-Beielstein. “Variable Reduction for Surrogate-Based Optimization “. Genetic and Evolutionary Computation Conference 2020.
  87. Falchi,L. Gentile, E. Minisci,“Launcher Vehicle Aerothermodynamics and Fairing Separation Altitude Effects on Payload Temperature”. European Conference for Aeronautics and Space Sciences 2019 (EUCASS ’19).
  88. Cho, D. Giugliano, L. Gentile, H. Chen.“Cyclic plasticity and creep-cyclic plasticity behaviours of the SiC/Ti-6242 Particulate Reinforced Titanium Matrix Composites under thermo-mechanical loadings”. Composites Structures.
  89. Rehbach, L. Gentile, T. Bartz-Beielstein. “Feature Selection for Surrogate Model-Based Optimization”.In Proceedings of the Genetic and Evolutionary Computation Conference 2019 (GECCO ’19).
  90. Gentile, M. Zaefferer, D. Giugliano, H. Chen, Bartz-Beielstein. 2018. “Surrogate assisted optimization of particle reinforced metal matrix composites”. In Proceedings of the Genetic and Evolutionary Computation Conference 2018 (GECCO ’18).
  91. Gentile, D. Giugliano, E. Cestino, G. Frulla, E. Minisci. 2018. “Optimisation based analysis of the effect of particle spatial distribution on the elastic behaviour of PRMMC”. In Proceedings of the European Conference on Computational Mechanics (ECCM 6).
  92. Bartz-Beielstein,L. Gentile, and M. Zaefferer, “In a nutshell: sequential parameter optimization” TH Köln2017-12-12 2017.
  93. A. del Val, O. P. Le Matre, O. Chazot, T. E. Magin, P. M. Congedo. On the Inference of Chemical Model Parameters for Tomorrows Space Journeys: an Overview on Reusable and Ablative Space Systems. Presentation at Optimization in Space Engineering (OSE5), Ljubljana, November 2019
  94. A. del Val, O. P. Le Matre, O. Chazot, T. E. Magin, P. M. Congedo. Bayesian Calibration of Gas/Surface Interaction Models for Thermal Protection Materials under Spacecraft Re-entry Conditions. Presentation at ICIAM 2019, Valencia University, Valencia, Spain, 15-19 July 2019.
  95. Luis, A. del Val, O. Chazot. Characterization under Uncertainty of Catalytic Phenomena in Ceramic Matrix Composites Materials for Spacecraft Thermal Protection Systems, 8th European Conference for Aeronautics and Aerospace Sciences (EUCASS), Madrid, June 2019. Shortlisted for best student paper award.
  96. A. del Val, O. P. Le Matre, O. Chazot, T. E. Magin, P. M. Congedo. Robust calibration of the catalytic properties of thermal protection materials: Application to plasma wind tunnel experiments. Presentation at the Uncertainty Quantification & Optimization Conference, Sorbonne University, Paris, France, 18-20 March 2019
  97. A. del Val, O. P. Le Matre, O. Chazot, T. E. Magin, P. M. Congedo. Stochastic inference of the catalytic properties of thermal protection materials from plasma wind tunnel experiments. Presentation at the 7th European Conference on Computational Fluid Dynamics, ECCOMAS, Glasgow, UK, 11-15 June 2018.
  98. A. del Val, O. Chazot, T. E. Magin. Uncertainty Treatment Applications: High-Enthalpy Flow Ground Testing, In Optimization Under Uncertainty with Applications to Aerospace Engineering, Ed. M. Vasile, Springer Nature 2020
  99. A. del Val. Bayes goes to Space: inferring chemical model parameters for tomorrow’s Space journeys. Stats4Grads, Durham University, Durham, UK, 6 Nov 2019.
  100. A. del Val. Characterization of spacecraft reusable heat shield materials from plasma wind tunnel experiments: a Bayesian inference approach. ETSIAE, UPM, Madrid, Spain, 1 Oct 2018.
  101. A. del Val. Stochastic inference of the catalytic properties of thermal protection materials from plasma wind tunnel experiments . Entry Systems and Technology Division Seminar, NASA Ames Research Center, Moffet Field, CA, 30 August 2018.
  102. A. del Val, O. P. Le Matre, O. Chazot, T. E. Magin, P. M. Congedo. A surrogate based optimal likelihood function for the Bayesian calibration of catalytic recombination in atmospheric entry protection materials. (Under review)
  103. A. del Val, D. Luis, O. Chazot. Characterization under uncertainty of catalytic effciencies in ceramic matrix composite materials for spacecraft thermal protection systems. (Submitted)
  104. A. del Val, O. P. Le Matre, P. M. Congedo, T. E. Magin. Investigation of graphite ablation in nitrogen plasma flows using a Bayesian formulation. (Submitted)
  105. A. del Val, O. P. Le Matre, P. M. Congedo, T. E. Magin. Impact of flow modeling choices on the calibration of nitridation efficiencies and model selection through Bayesian evidence. (Under preparation)
  106. Basu, Tathagata, Einbeck, Jochen & Troffaes, Matthias, A sensitivity analysis and error bounds for the adaptive lasso, in Irigoien, I., Lee, D.-J., Martinez-Minaya, J. & Rodriguez-Alvarez, M.X. eds, International Workshop on Statistical Modelling. Bilbao, Universidad del Pais Vasco, 278-281, 2020.
  107. Basu, Tathagata, Troffaes, Matthias C. M. & Einbeck, Jochen, Binary Credal Classification Under Sparsity Constraints, in Lesot, Marie-Jeanne, Vieira, Susana, Reformat, Marek Z., Carvalho, Joao Paulo, Wilbik, Anna, Bouchon-Meunier, Bernadette & Yager, Ronald R. eds, Information Processing and Management of Uncertainty in Knowledge-Based Systems. Lisbon, Springer, 82-95, 2020.
  108. Troffaes, Matthias C. M. & Basu, Tathagata, A Cantelli-type inequality for constructing non-parametric p-boxes based on exchangeability, in Bock, Jasper De Campos, Cassio P. de Cooman, Gert de Quaeghebeur, Erik & Wheeler, Gregory eds, Proceedings of Machine Learning Research 103: ISIPTA’19. Ghent, PMLR, 386-393, 2019.
  109. Krpelik D., Coolen F., Aslett L., A Decomposition approach for Computation of Survival Signatures of Heterogeneous Systems with Subsystems with Shared Components, European Safety and Reliability Conference, ESREL2019, Hannover, 2019
  110. Krpelik D., Coolen F.P.A., Aslett L.J.M., Imprecise probability inference on masked multicomponent system, Advances in Intelligent Systems and Computing series, Vol. 832, p. 133-140, 2019
  111. Krpelik D., Huang X., Aslett L.J.M., Coolen F.P.A., Reliability assessment of phased mission systems subjected to epistemic uncertainty and optimisation of the phase ordering, SECESA, Glasgow, 2018
  112. Krpelik D., Coolen F.P.A., Aslett L.J.M., On Robust Markov Analysis for Reliability Assessment of Complex Systems using Imprecise Markov Chains, Proceedings of the International Conference on Information and Digital Technologies 2019, IDT 2019, Zilina, 2019
  113. Krpelik D., Aslett L.J.M., Coolen F.P.A., Simultaneous Sampling for Robust Markov Chain Monte Carlo Inference, In: UQOP:International Conference on Uncertainty Quantification Optimisation. 2020 (accepted)
  114. Reis, J. F., Le Maître O. P., Congedo, P. M. and Mycek, P. Stochastic Preconditioning of Domain Decomposition Methods for Elliptic Equations with Random Coefficients, Methods Appl. Mech. Engrg, 2020 (submitted).
  115. Reis, J. F., Le Maître O. P., Congedo, P. M. and Mycek, P. Stochastic Preconditioners for Domain Decomposition Methods, UQOP2020 Conference Proceeding, 2020. (accepted)
  116. Gentile, L., Greco, C., Minisci, E., Bartz-Beielstein., T., & Vasile, M., (2020, under review) Satellite tracking with Constrained Budget via Structured-Chromosome Genetic Algorithms. Optimisation and Engineering.
  117.  

    Alexander Erreygers, Jasper De Bock. Bounding inferences for large-scale continuous-time Markov chains: A new approach based on lumping and imprecise Markov chains. International Journal of Approximate Reasoning, 115:96-133. December 2019.

  118. Alexander Erreygers, Jasper De Bock, Gert de Cooman & Arthur Van Camp. Optimal control of a linear system subject to partially specified input noise. International Journal of Robust and Nonlinear Control, 29(12):3892-3914. April 2019.

  119. Alexander Erreygers, Cristina Rottondi, Giacomo Verticale & Jasper De Bock. Imprecise Markov Models for Scalable and Robust Performance Evaluation of Flexi-Grid Spectrum Allocation Policies. IEEE Transactions on Communications, 66(11):5401–5414. November 2018.

  120. Floris Persiau, Jasper De Bock & Gert de Cooman. Computable randomness is about more than probabilities. Lecture Notes in Computer Science, vol 12322 (Proceedings of SUM 2020): pp. 172-186. September 2020.

  121. Natan T’Joens, Jasper De Bock & Gert de Cooman. In Search of a Global Belief Model for Discrete-Time Uncertain Processes. Proceedings of Machine Learning Research, Volume 103 (Proceedings of ISIPTA 2019): pp. 377-385. July 2019.

  122. Alexander Erreygers & Jasper De Bock. First Steps Towards an Imprecise Poisson Process. Proceedings of Machine Learning Research, Volume 103 (Proceedings of ISIPTA 2019): pp. 175-184. July 2019.

  123. Alexander Erreygers & Jasper De Bock. Computing Inferences for Large-Scale Continuous-Time Markov Chains by Combining Lumping with Imprecision. Uncertainty Modelling in Data Science (Proceedings of SMPS 2018): pp. 78-86. September 2018. (Best paper award).

  124. Jasper De Bock. Independent natural extension for infinite spaces: Williams-coherence to the rescue. Proceedings of Machine Learning Research, Volume 62 (Proceedings of ISIPTA 2017): pp. 121-132. July 2017.