Release Notes

MindSpore Flow 0.3.0 Release Notes

Major Feature and Improvements

Data Driven

  • [STABLE] Burgers_SNO/Navier_Stokes_SNO2D/Navier_Stokes_SNO3D: Applications sovling one-dimension Burgers Equation, two/three-dimension Navier Stokes Equation by Spectral Neural Operator under data driven method are added.

  • [STABLE] API-SNO1D/2D/3D: Spectral Neural Operator (including SNO and U-SNO) APIs are added, utilizing polynomial transformations to transform computations into a spectral space similar to FNO architecture. Its advantage lies in effectively reducing system bias caused by aliasing errors.

  • [STABLE] API-Attention: Refactoring most commonly used Transformer class networks such as Attention, MultiHeadAttention, AttentionBlock, and ViT network interfaces.

  • [STABLE] API-Diffusion: A complete set of training and inference interfaces for diffusion models are added with support of two mainstream diffusion methods of DDPM and DDIM. Meanwhile the entire process of diffusion model training and inference can be completed through the simple and easy-to-use interfaces of Diffusion Scheduler, Diffusion Trainer, Diffusion Pipeline, and Diffusion Transformer.

  • [STABLE] API-Refactor_Core: Refactor of mindflow.core by fusion of mindflow.common, mindflow.loss and mindflow.operators.

  • [RESEARCH] CascadeNet: CascadeNet case is added, It uses surface pressure, Reynolds number, and a small number of wake velocity measurement points as inputs to predict the spatiotemporal field of cylinder wake pulsation velocity through a generative adversarial network with scale transfer topology structure.

  • [RESEARCH] MultiScaleGNN: A multi-scale graph neural network case to solve the large-scale pressure Poisson equation is added, which supports the use of projection method (or fractional step method) to solve incompressible Navier Stokes equations.

  • [RESEARCH] TurbineUQ: A case study of turbine stage flow field prediction and uncertainty optimization design is added with a combination of Monte Carlo method with deep learning methods to quantitative evaluation of uncertainty.

Data-Mechanism Fusion

  • [STABLE] PhyMPGN: An application of PhyMPGN, a physical equation solving model based on graph neural networks for the problem of flow around a cylinder is added. PhyMPGN can solve Burgers, FitzHugh-Nagumo, Gray-Scott and other equations in unstructured grids. Related paper has been received as ICLR 2025 Spotlight.

  • [RESEARCH] Heat_Conduction: A case study of steady-state heat conduction physics field prediction driven by data and physics is added.

  • [RESEARCH] SuperPosition: SDNO, an operator neural network based on the superposition principle, is added for predicting the temperature field of complex flow patterns in aircraft engine internal flow cascades.

Physics Driven

  • [RESEARCH] NSFNets: Navier Stokes Flow Networks (NSFNets) are added. It is a highly cited paper for solving ill posed problems (such as partially missing boundary conditions or inversion problems.

Solver

  • [STABLE] CBS solver: Application of CBS acoustic equation solver for solving two-dimensional acoustic equations in complex parameter fields is added. The CBS solver solves the acoustic equation in the frequency domain and has spectral accuracy in all spatial directions, with higher accuracy than the finite difference method. Reference: Osnabrugge et al. 2016

Optimizer

  • [STABLE] API-AdaHessian second-order optimizer: AdaHessian second-order optimizer based on the second-order information provided by the diagonal elements of the Hessian matrix for optimization calculations is added. Tests achieved a loss reduction over 20% compared with Adam under the same number of steps.

Foundation Model

  • [RESEARCH] PDEformer: PDEformer supports to solve one dimensional/two dimensional general partial differential equations with time with a superior of accuracy to domain model by foundation model under Zero-Shot occasions.

Contributors

Thanks to the following developers for their contributions:

hsliu_ustc, gracezou, mengqinghe0909, Yi_zhang95, b_rookie, WhFanatic, xingzhongfan, juliagurieva, GQEm, chenruilin2024, ZYF00000, chenchao2024, wangqineng2024, BingyangWu-pkusms21, Bochengz, functoreality, huangxiang360729, ChenLeheng, juste_une_photo.

Contributions to the project in any form are welcome!