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 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |