Determining over- and under-constrained systems of equations using structural constraint delta. - David Broman; Kaj Nyström; Peter Fritzson (GPCE, 2006)

On Monte Carlo algorithms applied to Dirichlet problems for parabolic operators in the setting of time-dependent domains. - Kaj Nyström; Thomas Önskog (Monte Carlo Methods Appl., 2009)

Adaptive stochastic weak approximation of degenerate parabolic equations of Kolmogorov type. - Marie Frentz; Kaj Nyström (J. Comput. Appl. Math., 2010)

Pricing and hedging of financial derivatives using a posteriori error estimates and adaptive methods for stochastic differential equations. - Kaj Nyström; Thomas Önskog (J. Comput. Appl. Math., 2010)

Optimal switching problems under partial information. - Kai Li; Kaj Nyström; Marcus Olofsson (Monte Carlo Methods Appl., 2015)

A unified deep artificial neural network approach to partial differential equations in complex geometries. - Jens Berg; Kaj Nyström (Neurocomputing, 2018)

Neural ODEs as the Deep Limit of ResNets with constant weights. - Benny Avelin; Kaj Nyström (CoRR, 2019)

Data-driven discovery of PDEs in complex datasets. - Jens Berg; Kaj Nyström (J. Comput. Phys., 2019)

Solving the Dirichlet problem for the Monge-Ampère equation using neural networks. - Kaj Nyström; Matias Vestberg (CoRR, 2021)

Deep learning, stochastic gradient descent and diffusion maps. - Carmina Fjellström; Kaj Nyström (CoRR, 2022)

Hawkes-based models for high frequency financial data. - Kaj Nyström; Changyong Zhang (J. Oper. Res. Soc., 2022)