Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | 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. |