Prof. Dan Gabriel Cacuci

Department of Mechanical Engineering, University of South Carolina, USA

E-mail: cacuci@cec.sc.edu

Qualifications

1978 Ph.D., New York Applied Physics and Nuclear Engineering, Columbia University, USA

1977 M. Phil., New York Applied Physics and Nuclear Engineering, Columbia University, USA

1973 M.Sc., New York Nuclear Science and Engineering, Columbia University, USA

Publications (Selected Books)

1. Dan Gabriel Cacuci, Sensitivity and Uncertainty Analysis: Theory, Volume 1, 285 pages, Chapman &
   Hall/CRC, Boca Raton, 2003.
2. D.G. Cacuci, M. Ionescu-Bujor, and M.I. Navon Sensitivity and Uncertainty Analysis: Applications to Large Scale Systems, Volume 2, 352 pages, Chapman &
   Hall/CRC, Boca Raton, 2005.
   
3. Dan Gabriel Cacuci (Editor), Handbook of Nuclear Engineering, Five Volumes, ca. 3600 pages, 400 illustrations;  ISBN: 978-0-387-98150-5, Springer New
   York/Berlin, 2010.
4. D.G. Cacuci, M.I. Navon, and M. Ionescu-Bujor, Computational Methods for Data Evaluation and Assimilation, 337 pages, Chapman & Hall/CRC, Boca Raton, 2014.
5. Dan Gabriel Cacuci, The Second-Order Adjoint Sensitivity Analysis Methodology, 305 pages, Taylor & Francis/CRC Press, Boca Raton, 2018.
   ISBN 978-1-4978-2648-1
6. Dan Gabriel Cacuci, BERRU Predictive Modeling: Best Estimate Results with Reduced Uncertainties, 451 pages, Springer Verlag GmbH, Berlin, Germany, 2019;
   ISBN 978-3-662-58395-1; https://doi.org/10.1007/978-3-662-58395-1.
7. Dan Gabriel Cacuci, The n^th-Order Comprehensive Adjoint Sensitivity Analysis Methodology (n^th-CASAM): Overcoming the Curse of Dimensionality in Sensitivity
   and Uncertainty Analysis, Volume I: Linear Systems, 362 pages, Springer Nature Switzerland, Cham, 2022; https://doi.org/10.1007/978-3-030-96364-4.
8. Dan Gabriel Cacuci and Ruixian Fang, The n^th-Order Comprehensive Adjoint Sensitivity Analysis Methodology (n^th-CASAM): Overcoming the Curse of
   Dimensionality in Sensitivity and Uncertainty Analysis, Volume II: Application to a Large-Scale System, 463 pages, Springer Nature Switzerland, Cham, 2023,
   https://doi.org/10.1007/978-3-031-19635-5.
9. Dan Gabriel Cacuci, The n^th-Order Comprehensive Adjoint Sensitivity Analysis Methodology (n^th-CASAM): Overcoming the Curse of Dimensionality in Sensitivity
   and Uncertainty Analysis, Volume III: Nonlinear Systems, 369 pages, Springer Nature Switzerland, Cham, 2023, https://doi.org/10.1007/978-3-031-22757-8.
10. Dan Gabriel Cacuci, Advances in High-Order Predictive Modeling: Reducing Uncertainties, Taylor & Francis/CRC Boca Raton, (under contract; estimated submittal:
    12/2023).
    

Publications (Selected Papers)

1. Cacuci, D.G. “Fourth-Order Predictive Modelling: II. 4th-BERRU-PM Methodology for Combining Measurements with Computations to Obtain Best-Estimate Results
   with Reduced Uncertainties,” Am. J. Comp. Math, 2023, 13, 439-475. https://doi.org/10.4236/ajcm.2023.134025
2. Cacuci, D.G. “Fourth-Order Predictive Modelling: I. General Purpose Closed-Form Fourth-Order Moments Constrained MaxEnt Distribution,” Am. J. Comp. Math,
   2023, 13, 413-438. https://doi.org/10.4236/ajcm.2023.134024
3. Ruixian Fang and Dan G. Cacuci “4th-Order-SENS: A Software Module for Efficient and Exact 4th-Order Sensitivity Analysis of Neutron Particle Transport,”
   Nucl. Sci. Eng.,. DOI: 10.1080/00295639.2023.2255725 (16 Oct 2023)
4. Pain, Christopher; Tehrani, Ali; Heaney, Claire; Chen, Boyang; Nwegbu, Kenechukwu; Phillips, Toby; Jones, Alan; Dargaville, Steven; Buchan, Andrew; Smith,
   Paul; Bankhead, Mark; Cacuci, Dan; “Computational Modeling in Nuclear Science and Engineering: The Role of Artificial Intelligence,” Nuclear Future, 43, 42-50,
   September/October 2023.
5. Cacuci, D.G. "Computation of High-Order Sensitivities of Model Responses to Model Parameters. II: Introducing the Second-Order Adjoint Sensitivity Analysis
   Methodology for Computing Response Sensitivities to Functions/Features of Parameters,” Energies, 16, 2023, 6356. https://doi.org/10.3390/en16176356
6. Cacuci, D.G. " Computation of High-Order Sensitivities of Model Responses to Model Parameters. I: Review of Underlying Motivation and Current Methods,”
   Energies, 16, 2023, 6355. https://doi.org/10.3390/en16176355
7. Ruixian Fang, Dan G. Cacuci “Demonstrative Application to an OECD/NEA Reactor Physics Benchmark of the 2nd-BERRU-PM Method. II: Nominal Computations
   Apparently Inconsistent with Measurements,” Energies, 16, 5614, 2023; https://doi.org/10.3390/en16155614
8. Cacuci D. G. and Fang, R., “Demonstrative Application to an OECD/NEA Reactor Physics Benchmark of the 2nd-BERRU-PM Method. I: Nominal Computations
   Consistent with Measurements,” Energies, 16, 5552, 2023. https://doi.org/10.3390/en16145552
9. R. Fang and Cacuci, D.G. "Second-Order MaxEnt Predictive Modelling Methodology. III: Illustrative Application to a Reactor Physics Benchmark," Am. J. Comp.
   Math., 13, 295-322, 2023; https://doi.org/10.4236/ajcm.2023.132015
10. Cacuci, D.G. "Second-Order MaxEnt Predictive Modelling Methodology. II: Probabilistically Incorporated Computational Model (2nd-BERRU-PMP)," Am. J. Comp.
    Math., 13, 267-294, 2023; https://doi.org/10.4236/ajcm.2023.132014
11. Cacuci, D.G. "Second-Order MaxEnt Predictive Modelling Methodology. I: Deterministically Incorporated Computational Model (2nd-BERRU-PMD),” Am. J. Comp.
    Math., 13, 236-266, 2023; https://doi.org/10.4236/ajcm.2023.132013
12. Cacuci, D.G. “Perspective on Predictive Modeling: Current Status, New High-Order Methodology and Outlook for Energy Systems,” Energies, 2023, 16, 933.
    https://doi.org/10.3390/en16020933