Transport of phase space densities through tetrahedral meshes using discrete flow mapping. - Janis Bajars; David J. Chappell; Niels Søndergaard; Gregor Tanner (J. Comput. Phys., 2017)

Improved Approximation of Phase-Space Densities on Triangulated Domains Using Discrete Flow Mapping with p-Refinement. - Janis Bajars; David J. Chappell; Timo Hartmann; Gregor Tanner (J. Sci. Comput., 2017)

A boundary integral method for modelling vibroacoustic energy distributions in uncertain built up structures. - Janis Bajars; David J. Chappell (J. Comput. Phys., 2018)

Modelling uncertainties in phase-space boundary integral models of ray propagation. - Janis Bajars; David J. Chappell (Commun. Nonlinear Sci. Numer. Simul., 2020)

Data-driven intrinsic localized mode detection and classification in one-dimensional crystal lattice model. - Janis Bajars; Filips Kozirevs (CoRR, 2021)

Dimensionality Reduction with Proper Symplectic Decomposition for Learning Hamiltonian Dynamics. - Janis Bajars (NUMTA (2), 2023)

Locally-symplectic neural networks for learning volume-preserving dynamics. - Janis Bajars (J. Comput. Phys., 2023)

Spectral Properties of Exact Polarobreathers in Semiclassical Systems. - Juan F. R. Archilla; Janis Bajars (Axioms, 2023)

Unconstrained Parametrization of Proper Symplectic Decomposition for Learning Hamiltonian Dynamics. - Janis Bajars; Davis Kalvans; Dmitry Gromov (EUSFLAT (2), 2025)

Structure-preserving dimensionality reduction for learning Hamiltonian dynamics. - Janis Bajars; Davis Kalvans (J. Comput. Phys., 2025)