9 citations · 20 across the 7 of their papers we have counts for
8 papers
GENCO - A Unified Neural Solver Embedded in a Development Framework for Steady-State Grid Analysis
Alban Puech, Matteo Mazzonelli, Tamara R. Govindasamy +19
Foundation models are transforming business workflows and boosting productivity, yet they remain largely absent from engineering domains such as power system analysis, where strict…
TerraTorch: The Geospatial Foundation Models Toolkit
Carlos Gomes, Benedikt Blumenstiel, Joao Lucas de Sousa Almeida +7
TerraTorch is a fine-tuning and benchmarking toolkit for Geospatial Foundation Models built on PyTorch Lightning and tailored for satellite, weather, and climate data. It integrate…
Lossy Neural Compression for Geospatial Analytics: A Review
Carlos Gomes, Isabelle Wittmann, Damien Robert +24
Over the past decades, there has been an explosion in the amount of available Earth Observation (EO) data. The unprecedented coverage of the Earth's surface and atmosphere by satel…
Prithvi WxC: Foundation Model for Weather and Climate
Johannes Schmude, Sujit Roy, Will Trojak +26
Triggered by the realization that AI emulators can rival the performance of traditional numerical weather prediction models running on HPC systems, there is now an increasing numbe…
Multi-Spectral Remote Sensing Image Retrieval Using Geospatial Foundation Models
Benedikt Blumenstiel, Viktoria Moor, Romeo Kienzler +1
Image retrieval enables an efficient search through vast amounts of satellite imagery and returns similar images to a query. Deep learning models can identify images across various…
TensorBank: Tensor Lakehouse for Foundation Model Training
Romeo Kienzler, Leonardo Pondian Tizzei, Benedikt Blumenstiel +9
Storing and streaming high dimensional data for foundation model training became a critical requirement with the rise of foundation models beyond natural language. In this paper we…
CLAIMED -- the open source framework for building coarse-grained operators for accelerated discovery in science
Romeo Kienzler, Rafflesia Khan, Jerome Nilmeier +2
In modern data-driven science, reproducibility and reusability are key challenges. Scientists are well skilled in the process from data to publication. Although some publication ch…
CLAIMED, a visual and scalable component library for Trusted AI
Romeo Kienzler, Ivan Nesic
Deep Learning models are getting more and more popular but constraints on explainability, adversarial robustness and fairness are often major concerns for production deployment. Al…