December 09-13, 2017
The topic ‘Matrix Methods in Statistics’ is a branch of linear algebra and matrix theory containing a varieties of challenging problems in linear statistical models, statistical inference, and error analysis, having applications in various branches of science which concerns with measurements. Measurement and evaluation of nuclear data is one such very important branch of science having practical significance in designing and monitoring the advanced nuclear systems.
The design of advanced nuclear systems demands the assessment of confidence margins in nuclear power plant parameters. The errors in the design parameters of advanced nuclear systems due to errors arising from the uncertainties in basic nuclear data are addressed by the nuclear community by and large through covariance matrix theory. Total Monte Carlo methodology is also being developed for the purpose. A number of countries have studied error propagation in nuclear engineering using the errors and covariance among nuclear data. The basic evaluated nuclear data files, such as ENDF/B-VII.1 (USA), JEFF-3.2 (EU), JENDL-4.0 (Japan), TENDL-2015 (EU), etc., (see the IAEA-BARC Mirror website: www-nds.indcentre.org.in) are widely used in several applications including energy (e.g., advanced nuclear reactors) and non-energy (e.g., nuclear medicine). The evaluated nuclear data are specified in evaluated nuclear data files in the form of estimates of mean values and their co-variances. Efforts in India under the DAE-BRNS have been initiated in scientific evaluation of nuclear data, in particular, the extraction of recommended (best estimate) values and their covariance from uncertain, incomplete and error afflicted experimental data which require reasoning and inference techniques (Statistics, Bayesian, variants of Kalman Filter) in the face of uncertainties and correlations in raw experimental data.
The previous Theme meetings on nuclear data covariances were held in Manipal (2008), Vel-Tech, Chennai (2010), and in BARC, Mumbai (2013). The current Theme meeting was held during December 09-13, 2017. The first two days of the theme meeting had special lectures and tutorials for training the researchers in the measurement of covariance matrices, and the later three days of the DAE-BRNS Theme meeting covered presentations of research articles and invited talks. The December 11-13, 2017 part of the 5-day Theme meeting served parallel session in the conference ICLAA 2017 to enable mathematicians and nuclear data science experts interact together, as a unique event. All the sessions in the Theme meeting, December 09-13, 2017 were dedicated to discuss the importance and generation of covariance information which are essentially involved in the different steps of measurements, processing, evaluation and applications of nuclear data of importance to nuclear energy and non-energy (e.g., medical) applications, helping all researchers and young scholars involved in this activity.
|S. Ganesan||Bhabha Atomic Research Centre, Mumbai, India|
|Arjan Koning||International Atomic Energy Agency, Vienna, Austria|
|Rajeev Kumar||Bhabha Atomic Research Centre, Mumbai, India|
|Anek Kumar||Bhabha Atomic Research Centre, Mumbai, India|
|B. Lalremruata||Mizoram University, India|
|Helmut Leeb||University of Vienna, TU Wein, Vienna|
|Jayalekshmi M. Nair||V. E. S. Institute of Technology, India|
|Simo Puntanen||University of Tampere, Finland|
|Rudraswamy B||Bangalore University, India|
|Alok Saxena||Bhabha Atomic Research Centre, Mumbai, India|
|Peter Schillebeeckx||European Commission-Joint Research Centre, JRC, Belgium|
|Henrik Sjostrand||Uppsala University, Uppsala, Sweden|
|S. V. Suryanarayana||Bhabha Atomic Research Centre, Mumbai, India|
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