Learning to Assimilate in Chaotic Dynamical Systems
Authors: Michael McCabe, Jed Brown
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results across several benchmark systems highlight the improved effectiveness of our approach over widely-used data assimilation methods. In Section 5, where we see that amortized assimilation methods match or outperform conventional approaches across several benchmark systems with especially strong performance at smaller ensemble sizes. |
| Researcher Affiliation | Academia | Michael Mc Cabe Department of Computer Science University of Colorado Boulder michael.mccabe@colorado.edu Jed Brown Department of Computer Science University of Colorado Boulder jed@jedbrown.org |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at https://github.com/mikemccabe210/amortizedassimilation. |
| Open Datasets | No | For all experiments, we generate a training set consisting of 6000 sequences consisting of 40 assimilation steps each. The paper uses benchmark systems (Lorenz 96, Kuramoto-Shivashinsky, Vissio-Lucarini 20) to generate data rather than providing access to a pre-existing public dataset. |
| Dataset Splits | Yes | For all experiments, we generate a training set consisting of 6000 sequences consisting of 40 assimilation steps each. The validation set consists of a single sequence of an additional 1000 steps and the test set is a further 10,000 steps. |
| Hardware Specification | Yes | Models are trained on a single GTX 1070 GPU for 500 epochs |
| Software Dependencies | No | Models are developed in Py Torch [55] using the torchdiffeq [56] library for ODE integration. We compare performance against a set of widely used filtering methods for data assimilation implemented in the Python DAPPER library [58]. The paper mentions software tools but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | Am En F models are developed in Py Torch [55] using the torchdiffeq [56] library for ODE integration. Models are trained on a single GTX 1070 GPU for 500 epochs using the Adam [12] optimizer with initial learning rate 8e 4 with a warm-up over 50 iterations followed by halving the learning rate every 200 iterations. All experiments are repeated over ten independent noise samples and error bars indicate a single standard deviation. |