Diffusion Models Beat GANs on Topology Optimization
Authors: François Mazé, Faez Ahmed
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Compared to a state-of-the-art conditional GAN, our approach reduces the average error on physical performance by a factor of eight and produces eleven times fewer infeasible samples. Our work demonstrates the potential of using diffusion models in topology optimization and suggests a general framework for solving engineering optimization problems using external performance with constraint-aware guidance. 4 Empirical Evaluation We created three datasets to train the proposed models, which are made publicly available. |
| Researcher Affiliation | Academia | Franc ois Maz e1, Faez Ahmed1 1Massachusetts Institute of Technology francois.maze@etu.minesparis.psl.eu, {fmaze,faez}@mit.edu |
| Pseudocode | Yes | Algorithm 1 Regressor guidance for TO, given a conditional diffusion model (µθ(xt|xt+1, v, f, l), Σθ(xt|xt+1, v, f, l)) and a regressor cϕ(xt, v, f, l, bc). Algorithm 2 Guidance strategy for TO using Conditional Diffusion Model. |
| Open Source Code | Yes | We provide access to our data, code, and trained models at the following link: https://decode.mit.edu/projects/topodiff/. |
| Open Datasets | Yes | We created three datasets to train the proposed models, which are made publicly available. We provide access to our data, code, and trained models at the following link: https://decode.mit.edu/projects/topodiff/. |
| Dataset Splits | Yes | The main dataset is divided into training, validation, and testing as follows: ... 2. The validation data consist of 200 new combinations of constraints containing the same 42 boundary conditions; |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., CPU, GPU models, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like 'SIMP-based TO library To Py (Hunter et al. 2017)' and 'Solids Py: 2D-Finite Element Analysis with Python (Guar ın-Zapata and G omez 2020)' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper mentions 'hyperparameter tuning' and discusses 'gradient scale hyperparameters' (λc and λfm) in Section 3.4 and 4.3, and notes that a grid search was used for tuning, but it does not provide the specific numerical values of these hyperparameters or other training configurations such as learning rate or batch size. |