2 citations · 6 across the 8 of their papers we have counts for
10 papers
How Can Machine Learning Emulators Best Support Climate Science?
Luca Schmidt, Nina Effenberger, Vitus Benson +5
For decades, physics-based climate models have been used to provide insights for climate decision-making. Their application is, however, constrained by significant computational an…
Bridging CORDEX and CMIP6: Machine Learning Downscaling for Wind and Solar Energy Droughts in Central Europe
Nina Effenberger, Maxim Samarin, Maybritt Schillinger +1
Reliable regional climate information is essential for assessing the impacts of climate change and for planning in sectors such as renewable energy; yet, producing high-resolution…
EnScale: Temporally-consistent multivariate generative downscaling via proper scoring rules
Maybritt Schillinger, Maxim Samarin, Xinwei Shen +2
The practical use of future climate projections from global circulation models (GCMs) is often limited by their coarse spatial resolution, requiring downscaling to generate high-re…
Mesh-free Eulerian Physics-Informed Neural Networks
Fabricio Arend Torres, Marcello Massimo Negri, Monika Nagy-Huber +2
Physics-informed Neural Networks (PINNs) have recently emerged as a principled way to include prior physical knowledge in form of partial differential equations (PDEs) into neural…
Learning Invariances with Generalised Input-Convex Neural Networks
Vitali Nesterov, Fabricio Arend Torres, Monika Nagy-Huber +2
Considering smooth mappings from input vectors to continuous targets, our goal is to characterise subspaces of the input domain, which are invariant under such mappings. Thus, we w…
Learning Conditional Invariance through Cycle Consistency
Maxim Samarin, Vitali Nesterov, Mario Wieser +3
Identifying meaningful and independent factors of variation in a dataset is a challenging learning task frequently addressed by means of deep latent variable models. This task can…