DDDAS

Dynamic Data Driven Applications Systems: A New Paradigm for Application Simulations and Measurements. Dynamic Data Driven Application Systems (DDDAS) entails the ability to incorporate additional data into an executing application – these data can be archival or collected on-line; and in reverse, the ability of applications to dynamically steer the measurement process. The paradigm offers the promise of improving modeling methods, and augmenting the analysis and prediction capabilities of application simulations and the effectiveness of measurement systems. This presents the potential to transform the way science and engineering are done, and induce a major impact in the way many functions in our society are conducted, such as manufacturing, commerce, hazard management, medicine. Enabling this synergistic feedback and control-loop between application simulations and measurements requires novel application modeling approaches and frameworks, algorithms tolerant to perturbations from dynamic data injection and steering, and systems software to support the dynamic environments of concern here. Recent advances in complex applications, the advent of grid computing and of sensor systems, are some of the technologies that make it timely to embark in developing DDDAS capabilities. Research and development of such technologies requires synergistic multidisciplinary collaboration in the applications, algorithms, software systems, and measurements systems areas, and involving researchers in basic sciences, engineering, and computer sciences. The rest of the papers in the proceedings of this workshop provide examples of ongoing research developing DDDAS technologies within the context of specific and important application areas.


References in zbMATH (referenced in 29 articles )

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  1. He, Qizhi; Chen, Jiun-Shyan: A physics-constrained data-driven approach based on locally convex reconstruction for noisy database (2020)
  2. Ayensa-Jiménez, Jacobo; Doweidar, Mohamed H.; Sanz-Herrera, Jose A.; Doblaré, Manuel: An unsupervised data completion method for physically-based data-driven models (2019)
  3. Ayensa-Jiménez, Jacobo; Doweidar, Mohamed H.; Sanz-Herrera, Jose A.; Doblaré, Manuel: A new reliability-based data-driven approach for noisy experimental data with physical constraints (2018)
  4. Ibañez, Rubén; Abisset-Chavanne, Emmanuelle; Aguado, Jose Vicente; Gonzalez, David; Cueto, Elias; Chinesta, Francisco: A manifold learning approach to data-driven computational elasticity and inelasticity (2018)
  5. Neggers, Jan; Allix, Olivier; Hild, François; Roux, Stéphane: Big data in experimental mechanics and model order reduction: today’s challenges and tomorrow’s opportunities (2018)
  6. Rubio, Paul-Baptiste; Louf, François; Chamoin, Ludovic: Fast model updating coupling Bayesian inference and PGD model reduction (2018)
  7. Zhang, Jiaxin; Shields, Michael D.: The effect of prior probabilities on quantification and propagation of imprecise probabilities resulting from small datasets (2018)
  8. Korobenko, A.; Yan, J.; Gohari, S. M. I.; Sarkar, S.; Bazilevs, Y.: FSI simulation of two back-to-back wind turbines in atmospheric boundary layer flow (2017)
  9. Rao, Vishwas; Sandu, Adrian; Ng, Michael; Nino-Ruiz, Elias D.: Robust data assimilation using (L_1) and Huber norms (2017)
  10. Bauman, Paul T.; Stogner, Roy H.: GRINS: a multiphysics framework based on the libMesh finite element library (2016) ioport
  11. Marchand, Basile; Chamoin, Ludovic; Rey, Christian: Real-time updating of structural mechanics models using Kalman filtering, modified constitutive relation error, and proper generalized decomposition (2016)
  12. Oden, J. Tinsley; Lima, Ernesto A. B. F.; Almeida, Regina C.; Feng, Yusheng; Rylander, Marissa Nichole; Fuentes, David; Faghihi, Danial; Rahman, Mohammad M.; DeWitt, Matthew; Gadde, Manasa; Zhou, J. Cliff: Toward predictive multiscale modeling of vascular tumor growth, computational and experimental oncology for tumor prediction (2016)
  13. Rao, Vishwas; Sandu, Adrian: A time-parallel approach to strong-constraint four-dimensional variational data assimilation (2016)
  14. de Borst, René (ed.); Farhat, Charbel (ed.); Fish, Jacob (ed.); Harari, Isaac (ed.); Letallec, Patrick (ed.); Perić, Djordje (ed.): Special issue in tribute to Professor Ted Belytschko (2015)
  15. Deng, X.; Korobenko, A.; Yan, J.; Bazilevs, Y.: Isogeometric analysis of continuum damage in rotation-free composite shells (2015)
  16. Madireddy, Sandeep; Sista, Bhargava; Vemaganti, Kumar: A Bayesian approach to selecting hyperelastic constitutive models of soft tissue (2015)
  17. Prudencio, E. E.; Bauman, P. T.; Faghihi, D.; Ravi-Chandar, K.; Oden, J. T.: A computational framework for dynamic data-driven material damage control, based on Bayesian inference and model selection (2015)
  18. Rao, Vishwas; Sandu, Adrian: A posteriori error estimates for the solution of variational inverse problems (2015)
  19. Cencerrado, Andrés; Rodríguez, Roque; Cortés, Ana; Margalef, Tomàs: Urgency versus accuracy: dynamic data driven application system for natural hazard management (2012)
  20. Douglas, C.; Efendiev, Y.; Ewing, R.; Ginting, V.; Lazarov, R.; Cole, M.; Jones, G.: Least squares approach for initial data recovery in dynamic data-driven applications simulations (2010)

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