Self-regulating AGN jets in MHD simulations of cool-core clusters yield realistic FRI radio morphologies viewed along the jet axis and account for frequency-independent lobe extents via 1-50 μG fields allowing both young and old electrons to radiate.
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Cellular Sheaf Neural Operators use cell complexes, learned restriction maps, and structure-aware message passing to create discretization-aware neural surrogates that preserve constraints in multiphysics PDEs such as MHD.
Advection-only galactic wind models fail to reproduce observed vertical radio profiles without unrealistic velocities, synchrotron spectra are biased toward young electrons in dense regions, and bremsstrahlung/Coulomb losses cannot be neglected even when subdominant.
Electron cooling exobase position organizes Rosetta ion-density regimes at 67P and requires inclusion in near-nucleus models to avoid underestimating densities.
Presents 24 AREPO+IllustrisTNG zoom-in simulations of clusters above 10^15 solar masses showing converged magnetic fields with volume-averaged plasma beta approximately 100 inside R200 after z~1.2, declining Faraday rotation profiles, and intermittent Braginskii heating approaching cooling rates in
Energy-conserving numerics in CME simulations yield >2x higher kinetic energy by extending flare reconnection duration compared to non-conservative schemes.
Constrained transport outperforms Dedner's divergence cleaning in MHD simulations by avoiding spurious artifacts when magnetic fields are localized or timesteps vary suddenly.
Simulations of collapsing cores find that ε_ff varies with core definition via density threshold, open vs closed boundaries, and initial density, with higher values in low-mass cores due to lower infall rates.
Adapts FLASH code for MHD rigid body simulations to model non-magnetized planet-stellar wind interactions and examine viscosity influence on flow patterns.
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Cellular Sheaf Neural Operators for Structure-Preserving Surrogate Modeling of Constrained PDEs
Cellular Sheaf Neural Operators use cell complexes, learned restriction maps, and structure-aware message passing to create discretization-aware neural surrogates that preserve constraints in multiphysics PDEs such as MHD.