11 papers
How Hard Is Quantum Advantage? A Cloud Microphysics Stress Test for Variational Quantum Models
Felix Herbort, Ellen Sarauer, Daniel Ohl de Mello +7
Quantum machine learning (QML) could have the potential to leverage advantages of quantum over classical computing but still lacks strong evidence of actual improvements and scalab…
From stable online coupling to decade-long climate simulations: A machine learning parameterization for cloud microphysics in ICON
Ellen Sarauer, Mierk Schwabe, Philipp Weiss +3
The representation of cloud microphysics and its nonlinear character and scale-dependence is a remaining source of uncertainty in Earth system models (ESMs). Here, we develop and c…
Beyond the Training Data: Confidence-Guided Mixing of Parameterizations in a Hybrid AI-Climate Model
Helge Heuer, Tom Beucler, Mierk Schwabe +3
Persistent systematic errors in Earth system models (ESMs) arise from difficulties in representing the full diversity of subgrid, multiscale atmospheric convection and turbulence.…
Interpretable Neural Networks to Predict Momentum Fluxes of Orographic Gravity Waves
Elias Haslauer, Mierk Schwabe, Andreas Dörnbrack +4
State-of-the-art Earth system models (ESMs) cannot explicitly resolve many small-scale atmospheric processes such as atmospheric gravity waves, and thus must represent, or paramete…
Quantum-Enhanced Convergence of Physics-Informed Neural Networks
Nils Klement, Veronika Eyring, Mierk Schwabe
Partial differential equations (PDEs) form the backbone of simulations of many natural phenomena, for example in climate modeling, material science, and even financial markets. The…
Quantum Bayesian Optimization for the Automatic Tuning of Lorenz-96 as a Surrogate Climate Model
Paul J. Christiansen, Daniel Ohl de Mello, Cedric Brügmann +6
In this work, we propose a hybrid quantum-inspired heuristic for automatically tuning the Lorenz-96 model -- a simple proxy to describe atmospheric dynamics, yet exhibiting chaotic…