References in zbMATH (referenced in 21 articles )

Showing results 1 to 20 of 21.
Sorted by year (citations)

1 2 next

  1. Portone, Teresa; Moser, Robert D.: Bayesian inference of an uncertain generalized diffusion operator (2022)
  2. Regazzoni, F.; Salvador, M.; Dede’, L.; Quarteroni, A.: A machine learning method for real-time numerical simulations of cardiac electromechanics (2022)
  3. Arnald Puy, Samuele Lo Piano, Andrea Saltelli, Simon A. Levin: sensobol: an R package to compute variance-based sensitivity indices (2021) arXiv
  4. Biala, T. A.; Khaliq, A. Q. M.: A fractional-order compartmental model for the spread of the COVID-19 pandemic (2021)
  5. Brandstaeter, Sebastian; Fuchs, Sebastian L.; Biehler, Jonas; Aydin, Roland C.; Wall, Wolfgang A.; Cyron, Christian J.: Global sensitivity analysis of a homogenized constrained mixture model of arterial growth and remodeling (2021)
  6. Chathika Gunaratne, Ivan Garibay: NL4Py: Agent-based modeling in Python with parallelizable NetLogo workspaces (2021) not zbMATH
  7. Difrancesco, Rita Maria; van Schilt, Isabelle M.; Winkenbach, Matthias: Optimal in-store fulfillment policies for online orders in an omni-channel retail environment (2021)
  8. Frank, Anna S.; Larripa, Kamila; Ryu, Hwayeon; Snodgrass, Ryan G.; Röblitz, Susanna: Bifurcation and sensitivity analysis reveal key drivers of multistability in a model of macrophage polarization (2021)
  9. Katsuno, Eduardo Tadashi; Lidtke, Artur K.; Düz, Bülent; Rijpkema, Douwe; Dantas, João L. D.; Vaz, Guilherme: Estimating parameter and discretization uncertainties using a laminar-turbulent transition model (2021)
  10. Grigoriev, Vasiliy V.; Iliev, Oleg; Vabishchevich, Petr N.: Computational identification of adsorption and desorption parameters for pore scale transport in random porous media (2020)
  11. Grigoriev, Vasiliy V.; Iliev, Oleg; Vabishchevich, Petr N.: Computational identification of adsorption and desorption parameters for pore scale transport in periodic porous media (2020)
  12. Hadjimichael, A., Gold, D., Hadka, D., Reed, P.: Rhodium: Python Library for Many-Objective Robust Decision Making and Exploratory Modeling (2020) not zbMATH
  13. Lee, Taeksang; Bilionis, Ilias; Buganza Tepole, Adrian: Propagation of uncertainty in the mechanical and biological response of growing tissues using multi-fidelity Gaussian process regression (2020)
  14. Lejeune, Emma; Linder, Christian: Interpreting stochastic agent-based models of cell death (2020)
  15. Qian, George; Mahdi, Adam: Sensitivity analysis methods in the biomedical sciences (2020)
  16. Reis, Ruy Freitas; de Melo Quintela, Bárbara; de Oliveira Campos, Joventino; Gomes, Johnny Moreira; Rocha, Bernardo Martins; Lobosco, Marcelo; Weber dos Santos, Rodrigo: Characterization of the COVID-19 pandemic and the impact of uncertainties, mitigation strategies, and underreporting of cases in South Korea, Italy, and Brazil (2020)
  17. Rezaei, A.; Nakshatrala, K. B.; Siddiqui, F.; Dindoruk, B.; Soliman, M.: A global sensitivity analysis and reduced-order models for hydraulically fractured horizontal wells (2020)
  18. Robin A. Richardson, David W. Wright, Wouter Edeling, Vytautas Jancauskas, Jalal Lakhlili, Peter V. Coveney: EasyVVUQ: A Library for Verification, Validation and Uncertainty Quantification in High Performance Computing (2020) not zbMATH
  19. Lund, Alana; Dyke, Shirley J.; Song, Wei; Bilionis, Ilias: Global sensitivity analysis for the design of nonlinear identification experiments (2019)
  20. Ballester-Ripoll, Rafael; Paredes, Enrique G.; Pajarola, Renato: Tensor algorithms for advanced sensitivity metrics (2018)

1 2 next