4 papers · 1 filter
Learning Physical Operators using Neural Operators
Vignesh Gopakumar, Ander Gray, Dan Giles +5
Neural operators have emerged as promising surrogate models for solving partial differential equations (PDEs), but struggle to generalise beyond training distributions and are ofte…
Calibrated Physics-Informed Uncertainty Quantification
Vignesh Gopakumar, Ander Gray, Lorenzo Zanisi +5
Simulating complex physical systems is crucial for understanding and predicting phenomena across diverse fields, such as fluid dynamics and heat transfer, as well as plasma physics…
Scalable Data Assimilation with Message Passing
Oscar Key, So Takao, Daniel Giles +1
Data assimilation is a core component of numerical weather prediction systems. The large quantity of data processed during assimilation requires the computation to be distributed a…
Valid Error Bars for Neural Weather Models using Conformal Prediction
Vignesh Gopakumar, Joel Oskarrson, Ander Gray +5
Neural weather models have shown immense potential as inexpensive and accurate alternatives to physics-based models. However, most models trained to perform weather forecasting do…