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Prediction of casing critical buckling during shale gas hydraulic fracturing. (2019)
Journal Article
MOHAMMED, A.I., OYENEYIN, B., BARTLETT, M. and NJUGUNA, J. 2020. Prediction of casing critical buckling during shale gas hydraulic fracturing. Journal of petroleum science and engineering [online], 185, article ID 106655. Available from: https://doi.org/10.1016/j.petrol.2019.106655

Casing deformation during volume fracturing in shale gas horizontal wells is caused by both existing and induced stresses. These stresses jointly alter and compound the stress field around the casing leading to inefficient well stimulation as planned... Read More about Prediction of casing critical buckling during shale gas hydraulic fracturing..

On the definition of dynamic permutation problems under landscape rotation. (2019)
Conference Proceeding
ALZA, J., BARTLETT, M., CEBERIO, J. and MCCALL, J. 2019. On the definition of dynamic permutation problems under landscape rotation. In López-Ibáñez, M. (ed.) Proceedings of the 2019 Genetic and evolutionary computation conference companion (GECCO 2019), 13-17 July 2019, Prague, Czech Republic. New York: ACM [online], pages 1518-1526. Available from: https://doi.org/10.1145/3319619.3326840

Dynamic optimisation problems (DOPs) are optimisation problems that change over time. Typically, DOPs have been defined as a sequence of static problems, and the dynamism has been inserted into existing static problems using different techniques. In... Read More about On the definition of dynamic permutation problems under landscape rotation..

Bayesian network structure learning with integer programming: polytopes, facets and complexity. (2017)
Journal Article
CUSSENS, J., JÄRVISALO, M., KORHONEN, J.H. and BARTLETT, M. 2017. Bayesian network structure learning with integer programming: polytopes, facets and complexity. Journal of artificial intelligence research [online], 58, pages 185-229. Available from: https://doi.org/10.1613/jair.5203

The challenging task of learning structures of probabilistic graphical models is an important problem within modern AI research. Recent years have witnessed several major algorithmic advances in structure learning for Bayesian networks - arguably the... Read More about Bayesian network structure learning with integer programming: polytopes, facets and complexity..

Integer linear programming for the Bayesian network structure learning problem. (2015)
Journal Article
BARTLETT, M. and CUSSENS, J. 2017. Integer linear programming for the Bayesian network structure learning problem. Artificial intelligence [online], 244, pages 258-271. Available from: https://doi.org/10.1016/j.artint.2015.03.003

Bayesian networks are a commonly used method of representing conditional probability relationships between a set of variables in the form of a directed acyclic graph (DAG). Determination of the DAG which best explains observed data is an NP-hard prob... Read More about Integer linear programming for the Bayesian network structure learning problem..