13 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…
TerraMind: Large-Scale Generative Multimodality for Earth Observation
Johannes Jakubik, Felix Yang, Benedikt Blumenstiel +13
We present TerraMind, the first any-to-any generative, multimodal foundation model for Earth observation (EO). Unlike other multimodal models, TerraMind is pretrained on dual-scale…
How to Embed Matters: Evaluation of EO Embedding Design Choices
Luis Gilch, Isabelle Wittmann, Maximilian Nitsche +3
Earth observation (EO) missions produce petabytes of multispectral imagery, increasingly analyzed using large Geospatial Foundation Models (GeoFMs). Alongside end-to-end adaptation…
Breaking concentration barriers for quantum extreme learning on digital quantum processors
Timothée Dao, Ege Yilmaz, Ibrahim Shehzad +8
Reservoir computing leverages rich, non-linear dynamics to process temporal data. Quantum variants promise enhanced expressivity from high-dimensional Hilbert spaces, yet their pra…
NeuCo-Bench: A Novel Benchmark Framework for Neural Embeddings in Earth Observation
Rikard Vinge, Isabelle Wittmann, Jannik Schneider +4
We introduce NeuCo-Bench, a novel benchmark framework for evaluating (lossy) neural compression and representation learning in the context of Earth Observation (EO). Our approach b…
Quantizing Space and Time: Fusing Time Series and Images for Earth Observation
Gianfranco Basile, Johannes Jakubik, Benedikt Blumenstiel +2
We propose a task-agnostic framework for multimodal fusion of time series and single timestamp images, enabling cross-modal generation and robust downstream performance. Our approa…