21 citations · 21 across the 3 of their papers we have counts for
8 papers
GAIA: A Foundation Model for Operational Atmospheric Dynamics
Ata Akbari Asanjan, Olivia Alexander, Tom Berg +12
We introduce GAIA (Geospatial Artificial Intelligence for Atmospheres), a hybrid self-supervised geospatial foundation model that fuses Masked Autoencoders (MAE) with self-distilla…
Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications
Daniela Szwarcman, Sujit Roy, Paolo Fraccaro +33
This paper presents Prithvi-EO-2.0, a new geospatial foundation model that offers significant improvements over its predecessor, Prithvi-EO-1.0. Trained on 4.2 million global time…
Improving Variational Autoencoder using Random Fourier Transformation: An Aviation Safety Anomaly Detection Case-Study
Ata Akbari Asanjan, Milad Memarzadeh, Bryan Matthews +1
In this study, we focus on the training process and inference improvements of deep neural networks (DNNs), specifically Autoencoders (AEs) and Variational Autoencoders (VAEs), usin…
Sequential Reservoir Computing for Efficient High-Dimensional Spatiotemporal Forecasting
Ata Akbari Asanjan, Filip Wudarski, Daniel O'Connor +4
Forecasting high-dimensional spatiotemporal systems remains computationally challenging for recurrent neural networks (RNNs) and long short-term memory (LSTM) models due to gradien…
Surya: Foundation Model for Heliophysics
Sujit Roy, Johannes Schmude, Rohit Lal +30
Heliophysics is central to understanding and forecasting space weather events and solar activity. Despite decades of high-resolution observations from the Solar Dynamics Observator…
Assessing and Advancing the Potential of Quantum Computing: A NASA Case Study
Eleanor G. Rieffel, Ata Akbari Asanjan, M. Sohaib Alam +20
Quantum computing is one of the most enticing computational paradigms with the potential to revolutionize diverse areas of future-generation computational systems. While quantum co…