14 citations · 31 across the 11 of their papers we have counts for
13 papers
Shadow models of a quantum model for cloud cover and the influence of finite sampling noise
Hedwig Keller, Mierk Schwabe, Veronika Eyring
Quantum computing is a quickly growing field that is promising various advantages compared to conventional computing. However, currently stand-alone quantum applications are scarce…
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…
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…
Quantum Machine Learning for Climate Modelling
Mierk Schwabe, Lorenzo Pastori, Valentina Sarandrea +1
Quantum machine learning (QML) is making rapid progress, and QML-based models hold the promise of quantum advantages such as potentially higher expressivity and generalizability th…
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.…
Fisher Information, Training and Bias in Fourier Regression Models
Lorenzo Pastori, Veronika Eyring, Mierk Schwabe
Motivated by the growing interest in quantum machine learning, in particular quantum neural networks (QNNs), we study how recently introduced evaluation metrics based on the Fisher…
Quantum Neural Networks for Cloud Cover Parameterizations in Climate Models
Lorenzo Pastori, Arthur Grundner, Veronika Eyring +1
Long-term climate projections require running global Earth system models on timescales of hundreds of years and have relatively coarse resolution (from 40 to 160 km in the horizont…
Opportunities and challenges of quantum computing for climate modelling
Mierk Schwabe, Lorenzo Pastori, Inés de Vega +6
Adaptation to climate change requires robust climate projections, yet the uncertainty in these projections performed by ensembles of Earth system models (ESMs) remains large. This…
Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties with Deep Learning Multi-Member and Stochastic Parameterizations
Gunnar Behrens, Tom Beucler, Fernando Iglesias-Suarez +5
Deep learning is a powerful tool to represent subgrid processes in climate models, but many application cases have so far used idealized settings and deterministic approaches. Here…
Interpretable multiscale Machine Learning-Based Parameterizations of Convection for ICON
Helge Heuer, Mierk Schwabe, Pierre Gentine +2
Machine learning (ML)-based parameterizations have been developed for Earth System Models (ESMs) with the goal to better represent subgrid-scale processes or to accelerate computat…