Xiaocheng Shang

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Benedict LeimkuhlerUniversity of EdinburghVerified email at ed.ac.uk
Martin Kröger | KrogerProf. Computational Polymer Physics, ETH ZurichVerified email at mat.ethz.ch
Amos StorkeyProfessor of Machine Learning and AI, School of Informatics, University of Edinburgh, UKVerified email at ed.ac.uk
Zhanxing ZhuAssociate Professor, ECS, University of SouthamptonVerified email at soton.ac.uk
Hans Christian ÖttingerETH ZürichVerified email at mat.ethz.ch
Alessio AlexiadisReader, University of BirminghamVerified email at bham.ac.uk
Mostapha ARIANEChef de projet numérique et experimental / modelling and technical project managerVerified email at u-bourgogne.fr
Iwan Halim SahputraUniversitas Kristen PetraVerified email at petra.ac.id
Irene MoulitsasCranfield UniversityVerified email at cranfield.ac.uk
Amin Rahmat (PhD)Research Fellow, School of Mathematics, University of BirminghamVerified email at bham.ac.uk
Andrea AlbanoPrincipal Scientist, GSK Vx TRD DPVerified email at gsk.com
Carlos Alberto Duque-DazaAssociate Professor, Mechanical Engineering, Universidad Nacional de ColombiaVerified email at unal.edu.co
Khai Ching NgUniversity of Nottingham MalaysiaVerified email at nottingham.edu.my
Bart SlagterPhD candidate, Wageningen UniversityVerified email at wur.nl
Adugna MullissaUniversity of California, Los Angeles (UCLA)Verified email at ucla.edu
Johannes ReicheWageningen University (Associate Professor)Verified email at wur.nl
Wanda De KeersmaeckerPost-doctoral researcherVerified email at kuleuven.be
Martin HeroldGFZ Potsdam, University of Potsdam, Wageningen UniversityVerified email at gfz-potsdam.de
Veronique De SyMixed PixelVerified email at mixedpixel.nl
Johannes BallingPhD candidate at Wageningen UniversityVerified email at wur.nl

Xiaocheng Shang

University of Birmingham

Verified email at bham.ac.uk - Homepage

Molecular Dynamics Stochastic Modelling Scientific Computing Data Science


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Adaptive thermostats for noisy gradient systems B Leimkuhler, X Shang SIAM Journal on Scientific Computing 38 (2), A712-A736, 2016	61	2016
On the numerical treatment of dissipative particle dynamics and related systems B Leimkuhler, X Shang Journal of Computational Physics 280, 72-95, 2015	56	2015
Covariance-controlled adaptive Langevin thermostat for large-scale Bayesian sampling X Shang, Z Zhu, B Leimkuhler, AJ Storkey Advances in Neural Information Processing Systems 28, 2015	54	2015
Structure-preserving integrators for dissipative systems based on reversible–irreversible splitting X Shang, HC Öttinger Proceedings of the Royal Society A 476 (2234), 20190446, 2020	36	2020
Pairwise adaptive thermostats for improved accuracy and stability in dissipative particle dynamics B Leimkuhler, X Shang Journal of Computational Physics 324, 174-193, 2016	26	2016
Assessing numerical methods for molecular and particle simulation X Shang, M Kröger, B Leimkuhler Soft matter 13 (45), 8565-8578, 2017	20	2017
How to modify LAMMPS: From the prospective of a particle method researcher A Albano, E le Guillou, A Danzé, I Moulitsas, IH Sahputra, A Rahmat, ... ChemEngineering 5 (2), 30, 2021	14	2021
Intra-annual relationship between precipitation and forest disturbance in the African rainforest Y Gou, J Balling, V De Sy, M Herold, W De Keersmaecker, B Slagter, ... Environmental Research Letters 17 (4), 044044, 2022	12	2022
Time correlation functions of equilibrium and nonequilibrium Langevin dynamics: Derivations and numerics using random numbers X Shang, M Kroger Siam Review 62 (4), 901-935, 2020	11	2020
Accurate and robust splitting methods for the generalized Langevin equation with a positive Prony series memory kernel MH Duong, X Shang Journal of Computational Physics 464, 111332, 2022	9	2022
Accurate and efficient splitting methods for dissipative particle dynamics X Shang SIAM Journal on Scientific Computing 43 (3), A1929-A1949, 2021	4	2021
Extended stochastic dynamics: theory, algorithms, and applications in multiscale modelling and data science X Shang The University of Edinburgh, 2016	2	2016
Identifying Preliminary Risk Profiles for Dissociation in 16-to 25-year-olds Using Machine Learning R McGuinness, D Herring, X Wu, M Almandi, D Bhangu, L Collinson, ... Early Intervention in Psychiatry, 2025		2025
Identifying risk profiles for dissociation in 16-to 25-year-olds using machine learning. R McGuinness, D Herring, X Wu, M Almandi, D Bhangu, L Collinson, ... PsyArXiv, 2023		2023
How to modify LAMMPS A Albano, E le Guillou, A Danzé, I Moulitsas, IH Sahputra, A Rahmat, ...		2021
Time correlation functions of equilibrium and nonequilibrium Langevin dynamics X Shang, M Kröger

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