Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation
Authors: Da Long, Zhitong Xu, Guang Yang, Akil Narayan, Shandian Zhe
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of ACM-FD across several fundamental multi-physics systems. The code is released at https://github.com/ Bayesian AIGroup/ACM-FD. (...) Experiments: We evaluated ACM-FD on four fundamental multi-physics systems. (...) Finally, a series of ablation studies confirmed the effectiveness of the individual components of our method. |
| Researcher Affiliation | Academia | 1Kahlert School of Computing, University of Utah 2Department of Mathematics, University of Utah 3Scientific Computing and Imaging Institute, University of Utah. Correspondence to: Shandian Zhe <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Training(Z1, . . . , ZM, p(H)) (...) Algorithm 2 Generation (conditioned: Fc, target: Fs, target locations: Z s, all locations: Z = {Zk}) |
| Open Source Code | Yes | The code is released at https://github.com/ Bayesian AIGroup/ACM-FD. |
| Open Datasets | Yes | We used the 2D diffusion-reaction dataset provided from PDEBench (Takamoto et al., 2022). |
| Dataset Splits | Yes | We utilized 1,000 instances for training, 100 instances for validation, and 200 instances for testing. |
| Hardware Specification | Yes | All runtime experiments were conducted on a Linux cluster node equipped with an NVIDIA A100 GPU (40GB memory). |
| Software Dependencies | No | The paper mentions implementing parts in PyTorch ("reimplemented it using Py Torch. Our method, ACM-FD, was implemented with Py Torch as well.") and using tools like MATLAB PDE solver pdepe1 and scipy.interpolate.griddata, but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | Hyperparameter tuning was performed using the validation set. The details are provided in Appendix Section B. (...) In Appendix Section B: ACM-FD: the hyperparameters include the number of modes, which varies from {12, 16, 18, 20, 24}, the number of channels for channel lifting, which varies from {64, 128, 256}, the number of Fourier layers from, which varies from {3, 4, 5}, the length-scale of the SE kernel, which varies from {1e-2, 5e-3, 1e-3, 5e-4, 1e-4}. We used GELU activation. |