Recent publications
Article
Duong, MH & Shang, X 2022, 'Accurate and robust splitting methods for the generalized Langevin equation with a positive Prony series memory kernel', Journal of Computational Physics, vol. 464, 111332. https://doi.org/10.1016/j.jcp.2022.111332
Gou, Y, Balling, J, De Sy, V, Herold, M, De Keersmaecker, W, Slagter, B, Mullissa, A, Shang, X & Reiche, J 2022, 'Intra-annual relationship between precipitation and forest disturbance in the African rainforest', Environmental Research Letters, vol. 17, no. 4, 044044. https://doi.org/10.1088/1748-9326/ac5ca0
Shang, X 2021, 'Accurate and efficient splitting methods for dissipative particle dynamics', SIAM Journal on Scientific Computing, vol. 43, no. 3, A1929–A1949, pp. A1929-A1949. https://doi.org/10.1137/20M1336230
Albano, A, le Guillou, E, Danzé, A, Moulitsas, I, Sahputra, IH, Rahmat, A, Duque-Daza, CA, Shang, X, Ng, KC, Ariane, M & Alexiadis, A 2021, 'How to modify LAMMPS: from the prospective of a particle method researcher', ChemEngineering, vol. 5, no. 2, 30. https://doi.org/10.3390/chemengineering5020030
Shang, X & Öttinger, HC 2020, 'Structure-preserving integrators for dissipative systems based on reversible-irreversible splitting', Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 476, no. 2234, 20190446. https://doi.org/10.1098/rspa.2019.0446
Shang, X & Kröger, M 2020, 'Time correlation functions of equilibrium and nonequilibrium Langevin dynamics: derivations and numerics using random numbers', SIAM Review, vol. 62, no. 4, pp. 901-935. https://doi.org/10.1137/19M1255471
Shang, X, Kröger, M & Leimkuhler, B 2017, 'Assessing numerical methods for molecular and particle simulation', Soft Matter, vol. 13, no. 45, pp. 8565-8578. https://doi.org/10.1039/C7SM01526G
Leimkuhler, B & Shang, X 2016, 'Adaptive thermostats for noisy gradient systems', SIAM Journal on Scientific Computing, vol. 38, no. 2, pp. A712-A736. https://doi.org/10.1137/15m102318x
Leimkuhler, B & Shang, X 2016, 'Pairwise adaptive thermostats for improved accuracy and stability in dissipative particle dynamics', Journal of Computational Physics, vol. 324, pp. 174-193. https://doi.org/10.1016/j.jcp.2016.07.034
Leimkuhler, B & Shang, X 2015, 'On the numerical treatment of dissipative particle dynamics and related systems', Journal of Computational Physics, vol. 280, pp. 72-95. https://doi.org/10.1016/j.jcp.2014.09.008
Conference contribution
Shang, X, Zhu, Z, Leimkuhler, B & Storkey, AJ 2015, Covariance-controlled adaptive Langevin thermostat for large-scale Bayesian sampling. in Advances in Neural Information Processing Systems 28 . pp. 37-45. <https://papers.nips.cc/paper/5978-covariance-controlled-adaptive-langevin-thermostat-for-large-scale-bayesian-sampling.pdf>
Preprint
McGuinness, R, Herring, D, Wu, X, Almandi, M, Bhangu, D, Collinson, L, Shang, X & Černis, E 2023 'Identifying risk profiles for dissociation in 16- to 25-year-olds using machine learning' PsyArXiv. https://doi.org/10.31234/osf.io/j54v3
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