189 citations · 328 across the 10 of their papers we have counts for
10 papers
GEO-Bench: Toward Foundation Models for Earth Monitoring
Alexandre Lacoste, Nils Lehmann, Pau Rodriguez +14
Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to do…
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…
When happy accidents spark creativity: Bringing collaborative speculation to life with generative AI
Ziv Epstein, Hope Schroeder, Dava Newman
Generative AI techniques like those that synthesize images from text (text-to-image models) offer new possibilities for creatively imagining new ideas. We investigate the capabilit…
Digital Twin Earth -- Coasts: Developing a fast and physics-informed surrogate model for coastal floods via neural operators
Peishi Jiang, Nis Meinert, Helga Jordão +8
Developing fast and accurate surrogates for physics-based coastal and ocean models is an urgent need due to the coastal flood risk under accelerating sea level rise, and the comput…
WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data
Rupa Kurinchi-Vendhan, Björn Lütjens, Ritwik Gupta +2
The transition to green energy grids depends on detailed wind and solar forecasts to optimize the siting and scheduling of renewable energy generation. Operational forecasts from n…
PCE-PINNs: Physics-Informed Neural Networks for Uncertainty Propagation in Ocean Modeling
Björn Lütjens, Catherine H. Crawford, Mark Veillette +1
Climate models project an uncertainty range of possible warming scenarios from 1.5 to 5 degree Celsius global temperature increase until 2100, according to the CMIP6 model ensemble…
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…
Technology Readiness Levels for Machine Learning Systems
Alexander Lavin, Ciarán M. Gilligan-Lee, Alessya Visnjic +12
The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of dilig…
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…
Technology Readiness Levels for AI & ML
Alexander Lavin, Gregory Renard
The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence…