Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Physics-Informed Neural Network Policy Iteration: Algorithms, Convergence, and Verification
Authors: Yiming Meng, Ruikun Zhou, Amartya Mukherjee, Maxwell Fitzsimmons, Christopher Song, Jun Liu
ICML 2024 | Venue PDF | LLM Run Details | Input Tokens: 33,113 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,541 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present numerical examples to evaluate the performance of the proposed algorithms. |
| Researcher Affiliation | Academia | 1Coordinated Science Laboratory, University of Illinois Urbana-Champaign, Champaign Urbana, Illinois, United States 2Department of Applied Mathematics, University of Waterloo, Waterloo, Canada. |
| Pseudocode | Yes | Algorithm 1 Extreme Learning Machine Policy Iteration (ELM-PI) and Algorithm 2 Physics-Informed Neural Network Policy Iteration (PINN-PI) |
| Open Source Code | Yes | The code of these experiments can be found at https://git.uwaterloo. ca/hybrid-systems-lab/lyznet/. |
| Open Datasets | No | The paper uses synthetic and benchmark control problems defined by their dynamics, but does not refer to publicly available datasets with access information. |
| Dataset Splits | No | The paper mentions training and testing errors and the number of collocation points, but does not specify explicit training/validation/test dataset splits with percentages or counts. |
| Hardware Specification | Yes | ELM-PI experiments were run with an Intel Gold 6148 Skylake @ 2.4 GHz, and PINN-PI experiments were run with an NVidia V100SXM2 (16G memory). |
| Software Dependencies | No | The paper mentions software like 'Adam', 'd Real', and 'stable-baselines3' but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | All instances of ELM-PI and PINN-PI are run with the tanh activation function, unless otherwise noted. ... For PINN-PI, we train the network for 10,000 steps in each iteration with Adam. ... The iteration number of PI is set to be 10. |