Calibrated Physics-Informed Uncertainty Quantification
Authors: Vignesh Gopakumar, Ander Gray, Lorenzo Zanisi, Timothy Nunn, Daniel Giles, Matt Kusner, Stanislas Pamela, Marc Peter Deisenroth
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We further validate the efficacy of our method on neural PDE models for plasma modelling and shot design in fusion reactors. ... Section 5. Experiments |
| Researcher Affiliation | Collaboration | 1Centre for Artificial Intelligence, University College London 2Computing Division, UK Atomic Energy Authority 3Heudiasyc Laboratory 4Polytechnique Montr eal 5Mila Quebec AI Institute. |
| Pseudocode | Yes | C. Algorithmic Procedure 1. Set up the Neural PDE Solver (a) Define the PDE system of interest with its governing equations in a numerical solver (b) Train a neural network (e.g., Fourier Neural Operator) to approximate solutions to the PDE (c) Ensure the model can make predictions on new initial conditions / PDE coefficients |
| Open Source Code | Yes | The code and associated utility functions can be found in: https://github.com/gitvicky/CP-PRE |
| Open Datasets | No | The paper consistently describes generating data using specific solvers (e.g., "The dataset is generated using the JOREK code", "The solution for the Burgers equation is obtained by deploying a spectral solver") rather than providing access to pre-existing public datasets. |
| Dataset Splits | Yes | The dataset consists of 120 simulations (100 training, 20 testing) generated by solving the reduced MHD equations using JOREK with periodic boundary conditions. |
| Hardware Specification | Yes | The training was conducted on a single A100 GPU |
| Software Dependencies | No | The paper mentions software like PyTorch, TensorFlow, numpy, and Python, but does not provide specific version numbers for these or other libraries/solvers used. |
| Experiment Setup | Yes | Each model is trained for up to 500 epochs using the Adam optimiser (Kingma & Ba, 2015) with a step-decaying learning rate. The learning rate is initially set to 0.005 and scheduled to decrease by half after every 100 epochs. |