39 citations · 91 across the 19 of their papers we have counts for
8 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…
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…
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…
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…
Fourier-RNNs for Modelling Noisy Physics Data
Vignesh Gopakumar, Stanislas Pamela, Lorenzo Zanisi
Classical sequential models employed in time-series prediction rely on learning the mappings from the past to the future instances by way of a hidden state. The Hidden states chara…
Loss Landscape Engineering via Data Regulation on PINNs
Vignesh Gopakumar, Stanislas Pamela, Debasmita Samaddar
Physics-Informed Neural Networks have shown unique utility in parameterising the solution of a well-defined partial differential equation using automatic differentiation and residu…