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Found 6 papers, 6 papers with code
AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models
We present AstroCLIP, a strategy to facilitate the construction of astronomical foundation models that bridge the gap between diverse observational modalities.
Multiple Physics Pretraining for Physical Surrogate Models
We introduce multiple physics pretraining (MPP), an autoregressive task-agnostic pretraining approach for physical surrogate modeling.
xVal: A Continuous Number Encoding for Large Language Models
Large Language Models have not yet been broadly adapted for the analysis of scientific datasets due in part to the unique difficulties of tokenizing numbers.
Towards Stability of Autoregressive Neural Operators
Neural operators have proven to be a promising approach for modeling spatiotemporal systems in the physical sciences.
Learning to Assimilate in Chaotic Dynamical Systems
The accuracy of simulation-based forecasting in chaotic systems is heavily dependent on high-quality estimates of the system state at the time the forecast is initialized.
Mapper Comparison with Wasserstein Metrics
The challenge of describing model drift is an open question in unsupervised learning.
