James A Nichols
James A Nichols
Australian National University
Verified email at unsw.edu.au - Homepage
Cited by
Cited by
Quasi-Monte Carlo finite element methods for elliptic PDEs with lognormal random coefficients
IG Graham, FY Kuo, JA Nichols, R Scheichl, C Schwab, IH Sloan
Numerische Mathematik 131 (2), 329-368, 2015
Machine learning: applications of artificial intelligence to imaging and diagnosis
JA Nichols, HWH Chan, MAB Baker
Biophysical reviews 11 (1), 111-118, 2019
Fast CBC construction of randomly shifted lattice rules achieving O (n− 1+ δ) convergence for unbounded integrands over Rs in weighted spaces with POD weights
JA Nichols, FY Kuo
Journal of Complexity 30 (4), 444-468, 2014
Fractional order compartment models
CN Angstmann, AM Erickson, BI Henry, AV McGann, JM Murray, ...
SIAM Journal on Applied Mathematics 77 (2), 430-446, 2017
From stochastic processes to numerical methods: A new scheme for solving reaction subdiffusion fractional partial differential equations
CN Angstmann, IC Donnelly, BI Henry, BA Jacobs, TAM Langlands, ...
Journal of Computational Physics 307, 508-534, 2016
Greedy algorithms for optimal measurements selection in state estimation using reduced models
P Binev, A Cohen, O Mula, J Nichols
SIAM/ASA Journal on Uncertainty Quantification 6 (3), 1101-1126, 2018
A quantitative comparison of anti-HIV gene therapy delivered to hematopoietic stem cells versus CD4+ T cells
B Savkovic, J Nichols, D Birkett, T Applegate, S Ledger, G Symonds, ...
PLoS Comput Biol 10 (6), e1003681, 2014
A discrete time random walk model for anomalous diffusion
CN Angstmann, IC Donnelly, BI Henry, JA Nichols
Journal of Computational Physics 293, 53-69, 2015
Optimal reduced model algorithms for data-based state estimation
A Cohen, W Dahmen, R Devore, J Fadili, O Mula, J Nichols
arXiv preprint arXiv:1903.07938, 2019
A Banach spaces-based analysis of a new fully-mixed finite element method for the Boussinesq problem
E Colmenares, GN Gatica, S Moraga, X Zhao, C Su, A Cohen, ...
ESAIM, Math. Model. Numer. Anal, 2020
Reduced basis greedy selection using random training sets
A Cohen, D Wolfgang, R DeVore, J Nichols
ESAIM: Mathematical Modelling and Numerical Analysis, 2018
Subdiffusive discrete time random walks via Monte Carlo and subordination
JA Nichols, BI Henry, CN Angstmann
Journal of Computational Physics 372, 373-384, 2018
Quasi-Monte Carlo methods with applications to partial differential equations with random coefficients
JA Nichols
PhD Thesis, University of New South Wales, in preparation, 2014
Greedy measurement selection for state estimation
J Nichols, A Cohen, P Binev, O Mula
ScienceOpen Posters, 2018
Application of QMC methods to PDEs with random coefficients: a survey of analysis and implementation
F Kuo, J Dick, T Le Gia, J Nichols, I Sloan, I Graham, R Scheichl, ...
A Quantitative Comparison of Anti-HIV Gene Therapy Delivered to Hematopoietic
B Savkovic, J Nichols, D Birkett, T Applegate, S Ledger
Measurement selection for reduced model based state estimation
P Binev, A Cohen, O Mula, J Nichols
Book of Abstracts ENUMATH 2017, 68, 0
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