NeuralFluid: Nueral Fluidic System Design and Control with Differentiable Simulation
Authors: Yifei Li, Yuchen Sun, Pingchuan Ma, Eftychios Sifakis, Tao Du, Bo Zhu, Wojciech Matusik
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | By seamlessly incorporating our differentiable fluid simulator into a learning framework, we demonstrate successful design, control, and learning results that surpass gradient-free solutions in these benchmark tasks. |
| Researcher Affiliation | Academia | Yifei Li MIT CSAIL Yuchen Sun Georgia Institute of Technology Pingchuan Ma MIT CSAIL Eftychios Sifakis University of Wisconsin-Madison Tao Du Tsinghua University, Shanghai Qi Zhi Institute Bo Zhu Georgia Institute of Technology Wojciech Matusik MIT CSAIL |
| Pseudocode | No | The paper describes the numerical simulation steps in text and mathematical equations (e.g., in Section 2.3 and Appendix B) but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | We plan to release our code and documentation upon acceptance. |
| Open Datasets | No | The paper introduces 'a benchmark of design, control, and learning tasks on high-fidelity, high-resolution dynamic fluid environments' and 'a suite of Gym-like environments', which are custom setups for their experiments rather than publicly available datasets with specific access information or citations. |
| Dataset Splits | No | The paper does not specify explicit training, validation, or test dataset splits (e.g., percentages or sample counts) for the environments and tasks described, nor does it mention cross-validation setups. |
| Hardware Specification | Yes | The experiment runs on a workstation with an NVIDIA RTX A6000 GPU |
| Software Dependencies | No | The paper mentions software like C++, CUDA, pybind11 [13], and PyTorch [14] but does not provide specific version numbers for these software components or libraries. |
| Experiment Setup | Yes | Initial conditions are set for all optimizations using randomly sampled values. We use Adam as our optimizer. We summarize the simulation configuration and optimization configuration as well as relevant statistics in Table 1. |