activity
20202026
most citedGenerating Physically-Consistent Satellite Imagery for Climate Visualizations

35 citations · 85 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2026

PDE foundation models are skillful AI weather emulators for the Martian atmosphere

Johannes Schmude, Sujit Roy, Liping Wang +10

We show that AI foundation models that are pretrained on numerical solutions to a diverse corpus of partial differential equations can be adapted and fine-tuned to obtain skillful…

cs.CV2025

MeltwaterBench: Deep learning for spatiotemporal downscaling of surface meltwater

Björn Lütjens, Patrick Alexander, Raf Antwerpen +3

The Greenland ice sheet is melting at an accelerated rate due to processes that are not fully understood and hard to measure. The distribution of surface meltwater can help underst…

cs.LG2024★ 14 cited

The impact of internal variability on benchmarking deep learning climate emulators

Björn Lütjens, Raffaele Ferrari, Duncan Watson-Parris +1

Full-complexity Earth system models (ESMs) are computationally very expensive, limiting their use in exploring the climate outcomes of multiple emission pathways. More efficient em…

cs.LG2022★ 7 cited

Multiscale Neural Operator: Learning Fast and Grid-independent PDE Solvers

Björn Lütjens, Catherine H. Crawford, Campbell D Watson +2

Numerical simulations in climate, chemistry, or astrophysics are computationally too expensive for uncertainty quantification or parameter-exploration at high-resolution. Reduced-o…

cs.LG2021★ 1 cited

Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark

Alexandre Lacoste, Evan David Sherwin, Hannah Kerner +9

Recent progress in self-supervision shows that pre-training large neural networks on vast amounts of unsupervised data can lead to impressive increases in generalisation for downst…

cs.CV2021★ 35 cited

Generating Physically-Consistent Satellite Imagery for Climate Visualizations

Björn Lütjens, Brandon Leshchinskiy, Océane Boulais +11

Deep generative vision models are now able to synthesize realistic-looking satellite imagery. But, the possibility of hallucinations prevents their adoption for risk-sensitive appl…

cs.CV2020★ 28 cited

Physics-informed GANs for Coastal Flood Visualization

Björn Lütjens, Brandon Leshchinskiy, Christian Requena-Mesa +8

As climate change increases the intensity of natural disasters, society needs better tools for adaptation. Floods, for example, are the most frequent natural disaster, but during h…