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
Fractional order compartment models
CN Angstmann, AM Erickson, BI Henry, AV McGann, JM Murray, ...
SIAM Journal on Applied Mathematics 77 (2), 430-446, 2017
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
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
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
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
A Banach spaces-based analysis of a new fully-mixed finite element method for the Boussinesq problem
E Colmenares, GN Gatica, S Moraga
ESAIM: Mathematical Modelling and Numerical Analysis 54 (5), 1525-1568, 2020
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
Reduced basis greedy selection using random training sets
A Cohen, W Dahmen, R Devore, J Nichols
ESAIM: Mathematical Modelling and Numerical Analysis 54 (5), 1509-1524, 2020
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
Nonlinear reduced models for state and parameter estimation
A Cohen, W Dahmen, O Mula, J Nichols
arXiv preprint arXiv:2009.02687, 2020
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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