6 papers · 1 filter
Data-Efficient Neural Operator Training via Physics-Based Active Learning
Alicja Polanska, Lorenzo Zanisi, Vignesh Gopakumar +1
Solving partial differential equations with neural operators significantly reduces computational costs but remains bottlenecked by high training data requirements. Active learning…
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…
Leveraging AI modelling for FDS with Simvue: monitor and optimise for more sustainable simulations
James Panayis, Matt Field, Vignesh Gopakumar +4
There is high demand on fire simulations, in both scale and quantity. We present a multi-pronged approach to improving the time and energy required to meet these demands. We show t…
Guaranteed prediction sets for functional surrogate models
Ander Gray, Vignesh Gopakumar, Sylvain Rousseau +1
We propose a method for obtaining statistically guaranteed prediction sets for functional machine learning methods: surrogate models which map between function spaces, motivated by…
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…
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…