Articles with public access mandates - Anders SzepessyLearn more
Available somewhere: 11
Langevin molecular dynamics derived from Ehrenfest dynamics
A Szepessy
Mathematical Models and methods in applied sciences 21 (11), 2289-2334, 2011
Mandates: Swedish Research Council
Monte Carlo Euler approximations of HJM term structure financial models
T Björk, A Szepessy, R Tempone, GE Zouraris
BIT Numerical Mathematics 53, 341-383, 2013
Mandates: Swedish Research Council
Computational error estimates for Born–Oppenheimer molecular dynamics with nearly crossing potential surfaces
C Bayer, H Hoel, A Kadir, P Plecháč, M Sandberg, A Szepessy
Applied Mathematics Research eXpress 2015 (2), 329-417, 2015
Mandates: Swedish Research Council
Canonical quantum observables for molecular systems approximated by ab initio molecular dynamics
A Kammonen, P Plecháč, M Sandberg, A Szepessy
Annales Henri Poincaré 19, 2727-2781, 2018
Mandates: US Department of Defense, Swedish Research Council
Smaller generalization error derived for a deep residual neural network compared with shallow networks
A Kammonen, J Kiessling, P Plecháč, M Sandberg, A Szepessy, ...
IMA Journal of Numerical Analysis 43 (5), 2585-2632, 2023
Mandates: US Department of Defense, Swedish Research Council
Computable error estimates for finite element approximations of elliptic partial differential equations with rough stochastic data
EJ Hall, H Hoel, M Sandberg, A Szepessy, R Tempone
SIAM Journal on Scientific Computing 38 (6), A3773-A3807, 2016
Mandates: Swedish Research Council, Research Council of Norway
Canonical mean-field molecular dynamics derived from quantum mechanics
X Huang, P Plecháč, M Sandberg, A Szepessy
ESAIM: Mathematical Modelling and Numerical Analysis 56 (6), 2197-2238, 2022
Mandates: US Department of Defense, Swedish Research Council
An error estimate for symplectic Euler approximation of optimal control problems
J Karlsson, S Larsson, M Sandberg, A Szepessy, R Tempone
SIAM Journal on Scientific Computing 37 (2), A946-A969, 2015
Mandates: Swedish Research Council
Smaller generalization error derived for deep compared to shallow residual neural networks
A Kammonen, J Kiessling, P Plechác, M Sandberg, A Szepessy, ...
arXiv preprint arXiv:2010.01887, 2020
Mandates: US Department of Defense, Swedish Research Council
How accurate is Born-Oppenheimer molecular dynamics for crossings of potential surfaces
H Hoel, A Kadir, P Plechác, M Sandberg, A Szepessy
arXiv preprint arXiv:1305.3330, 2013
Mandates: Swedish Research Council
THE CLASSICAL LIMIT OF QUANTUM OBSERVABLES IN CONSERVATION LAWS OF FLUID DYNAMICS
M SANDBERG, A SZEPESSY
arXiv preprint arXiv:1702.04368, 2017
Mandates: Swedish Research Council
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