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..
RoPINN: Region Optimized Physics-Informed Neural Networks
Authors: Haixu Wu, Huakun Luo, Yuezhou Ma, Jianmin Wang, Mingsheng Long
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, Ro PINN consistently boosts the performance of diverse PINNs on a wide range of PDEs without extra backpropagation or gradient calculation. Code is available at this repository: https://github.com/thuml/Ro PINN. and 4 Experiments To verify the effectiveness and generalizability of our proposed Ro PINN, we experiment with a wide range of PDEs, covering diverse physics processes and a series of advanced PINN models. |
| Researcher Affiliation | Academia | Haixu Wu, Huakun Luo, Yuezhou Ma, Jianmin Wang, Mingsheng Long School of Software, BNRist, Tsinghua University, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Region Optimized PINN (Ro PINN) Input: number of iterations T, number of past iterations T0 retained to estimate the trust region, default region size r, trust region calibration value σ0 = 1, and initial PINN parameters θ0. Output: optimized PINN parameters θT . Initialize an empty buffer to record gradients as g. for t = 0 to T do // Region Optimization with Monte Carlo Approximation Sample points from neighborhood regions: S = {xi + ξi}|S| i=1, xi S, ξi U[0, r Calculate loss function Lt = L (uθt, S ) Update θt to θt+1 with optimizer (Adam [21], L-BFGS [27], etc) to minimize loss function Lt // Trust Region Calibration Record the gradient of parameters gt throughout optimization Update gradient buffer g by adding gt and keeping the latest T0 elements Trust region calibration with σt+1 = σ(g) end for |
| Open Source Code | Yes | Code is available at this repository: https://github.com/thuml/Ro PINN. |
| Open Datasets | Yes | Benchmarks For a comprehensive evaluation, we experiment with four benchmarks: 1D-Reaction, 1D-Wave, Convection and PINNacle [12]. and in references [12] Zhongkai Hao, Jiachen Yao, Chang Su, Hang Su, Ziao Wang, Fanzhi Lu, Zeyu Xia, Yichi Zhang, Songming Liu, Lu Lu, et al. Pinnacle: A comprehensive benchmark of physics-informed neural networks for solving pdes. ar Xiv preprint ar Xiv:2306.08827, 2023. |
| Dataset Splits | No | Table 4 provides Ntrain and Ntest counts for PINNacle benchmarks, but does not explicitly mention a separate validation split. For 1D-Reaction, 1D-Wave, and Convection, sampling points for training and evaluation are mentioned, but not a distinct validation set. |
| Hardware Specification | Yes | All experiments are implemented in Py Torch [34] and trained on a single NVIDIA A100 GPU. |
| Software Dependencies | No | All experiments are implemented in Py Torch [34] and trained on a single NVIDIA A100 GPU. Reference [34] points to 'Pytorch: An imperative style, high-performance deep learning library. In Neur IPS, 2019.', which does not provide a specific version number (e.g., PyTorch 1.x). |
| Experiment Setup | Yes | In Ro PINN (Algorithm 1), we select the multi-iteration hyperparameter T0 from {5, 10} and set the initial region size r = 10−4 for all datasets, where the trust region size will be adaptively adjusted to fit the PDE property during training. For 1D-Reaction, 1D-Wave and Convection, we follow [58] and train the model with L-BFGS optimizer [27] for 1,000 iterations. As for PINNacle, we strictly follow their official configuration [12] and train the model with Adam [21] for 20,000 iterations. Besides, for simplicity and fair comparison, we set the weights of PINN loss as equal, that is λ = 1 in Eq. (2). |