arXiv subjects

Christian Hüttig

Publications and source records attributed to Christian Hüttig.

4 recordsLinked to original sources

arXiv2026

Prospects for Mercury Observations with the BepiColombo Laser Altimeter (BELA): Implications for Estimating Surface Roughness from Laser Altimetry

Surface reflectance and roughness are key to understanding Mercury's geologic evolution. The BepiColombo Laser Altimeter (BELA) will measure these properties alongside topography, but predicting its performance requires realistic modeling of laser return pulse shapes, unlike a simplified Gaussian approximation in earlier models. We develop a comprehensive return-pulse simulation framework for BELA that includes non-Gaussian transmitted pulses, analog filtering in the receiver chain, and pulse-shape modification by footprint-scale topography. Using high-resolution lunar digital terrain models and synthesized fractal topography as Mercury analogs, we develop calibration and correction methods and estimate measurement errors expected in future BELA observations. Range errors average 1.5 m or less below 1000 km altitude, and energy errors are small enough to distinguish dark deposits, regolith, and exposed ice below 900 km. Pulse width proves unreliable as a roughness proxy under realistic conditions; we instead constrain roughness from digitized pulse shapes, underscoring the value of time-resolved data for future planetary altimeters.

astro-ph.EP↗

arXiv2025

Physics-based machine learning for mantle convection simulations

Mantle convection simulations are an essential tool for understanding how rocky planets evolve. However, the poorly known input parameters to these simulations, the non-linear dependence of transport properties on pressure and temperature, and the long integration times in excess of several billion years all pose a computational challenge for numerical solvers. We propose a physics-based machine learning approach that predicts creeping flow velocities as a function of temperature while conserving mass, thereby bypassing the numerical solution of the Stokes problem. A finite-volume solver then uses the predicted velocities to advect and diffuse the temperature field to the next time-step, enabling autoregressive rollout at inference. For training, our model requires temperature-velocity snapshots from a handful of simulations (94). We consider mantle convection in a two-dimensional rectangular box with basal and internal heating, pressure- and temperature-dependent viscosity. Overall, our model is up to 89 times faster than the numerical solver. We also show the importance of different components in our convolutional neural network architecture such as mass conservation, learned paddings on the boundaries, and loss scaling for the overall rollout performance. Finally, we test our approach on unseen scenarios to demonstrate some of its strengths and weaknesses.

astro-ph.EP↗

arXiv2024

Accelerating the discovery of steady-states of planetary interior dynamics with machine learning

Simulating mantle convection often requires reaching a computationally expensive steady-state, crucial for deriving scaling laws for thermal and dynamical flow properties and benchmarking numerical solutions. The strong temperature dependence of the rheology of mantle rocks causes viscosity variations of several orders of magnitude, leading to a slow-evolving stagnant lid where heat conduction dominates, overlying a rapidly-evolving and strongly convecting region. Time-stepping methods, while effective for fluids with constant viscosity, are hindered by the Courant criterion, which restricts the time step based on the system's maximum velocity and grid size. Consequently, achieving steady-state requires a large number of time steps due to the disparate time scales governing the stagnant and convecting regions. We present a concept for accelerating mantle convection simulations using machine learning. We generate a dataset of 128 two-dimensional simulations with mixed basal and internal heating, and pressure- and temperature-dependent viscosity. We train a feedforward neural network on 97 simulations to predict steady-state temperature profiles. These can then be used to initialize numerical time stepping methods for different simulation parameters. Compared to typical initializations, the number of time steps required to reach steady-state is reduced by a median factor of 3.75. The benefit of this method lies in requiring very few simulations to train on, providing a solution with no prediction error as we initialize a numerical method, and posing minimal computational overhead at inference time. We demonstrate the effectiveness of our approach and discuss the potential implications for accelerated simulations for advancing mantle convection research.

physics.flu-dyn↗

arXiv2022

RayPC: Interactive Ray Tracing Meets Parallel Coordinates

Large-scale numerical simulations of planetary interiors require dedicated visualization algorithms that are able to efficiently extract a large amount of information in an interactive and user-friendly way. Here we present a software framework for the visualization of mantle convection data. This framework combines real-time volume rendering, pathline visualization, and parallel coordinates to explore the fluid dynamics in an interactive way and to identify correlations between various output variables.

cs.GR↗