Increasing efficiency and accuracy of magnetic interaction calculations in colloidal simulation through machine learning.

Pan, Chunzhou; Mahmoudabadbozchelou, Mohammadamin; Duan, Xiaoli; Benneyan, James C; Jamali, Safa; Erb, Randall M

Pan, Chunzhou; Mahmoudabadbozchelou, Mohammadamin; Duan, Xiaoli; Benneyan, James C; Jamali, Safa; Erb, Randall M.

Affiliation

Pan C; Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02465, USA.
Mahmoudabadbozchelou M; Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02465, USA.
Duan X; Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02465, USA.
Benneyan JC; Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02465, USA.
Jamali S; Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02465, USA. Electronic address: s.jamali@northeastern.edu.
Erb RM; Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02465, USA. Electronic address: r.erb@neu.edu.

J Colloid Interface Sci ; 611: 29-38, 2022 Apr.

Article in En | MEDLINE | ID: mdl-34929436

Subject(s)

Machine Learning; Neural Networks, Computer; Algorithms; Computer Simulation; Magnetic Phenomena

Key words

Colloidal forces; Dipole model; Machine Learning; Magnetic particle interactions; Multi-fidelity neural network

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Neural Networks, Computer / Machine Learning Language: En Journal: J Colloid Interface Sci Year: 2022 Document type: Article

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