Public articles linked to the same research event.
arXiv The authors present AEGIS, a differentiable Mars climate model that couples Mars radiation, surface, CO2 frost, and regolith physics to the Dinosaur spectral dynamical core; at T21/L12 it completes stable ten-Mars-year integrations, reproduces the seasonal CO2 cycle while conserving the total atmosphere–frost CO2 inventory to very small drift, produces surface pressure that follows MOLA topography, and yields automatic-differentiation gradients that agree with finite differences for physical calibration and neural-closure training.
The authors present AEGIS, a differentiable Mars climate model that couples Mars radiation, surface, CO2 frost, and regolith physics to the Dinosaur spectral dynamical core; at T21/L12 it completes stable ten-Mars-year integrations, reproduces the seasonal CO2 cycle while conserving the total atmosphere–frost CO2 inventory to very small drift, produces surface pressure that follows MOLA topography, and yields automatic-differentiation gradients that agree with finite differences for physical calibration and neural-closure training.
The authors present AEGIS, a differentiable Mars climate model that couples Mars radiation, surface, CO2 frost, and regolith physics to the Dinosaur spectral dynamical core; at T21/L12 it completes stable ten-Mars-year integrations, reproduces the seasonal CO2 cycle while conserving the total atmosphere–frost CO2 inventory to very small drift, produces surface pressure that follows MOLA topography, and yields automatic-differentiation gradients that agree with finite differences for physical calibration and neural-closure training.
The authors present AEGIS, a differentiable Mars climate model that couples Mars radiation, surface, CO2 frost, and regolith physics to the Dinosaur spectral dynamical core; at T21/L12 it completes stable ten-Mars-year integrations, reproduces the seasonal CO2 cycle while conserving the total atmosphere–frost CO2 inventory to very small drift, produces surface pressure that follows MOLA topography, and yields automatic-differentiation gradients that agree with finite differences for physical calibration and neural-closure training.