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@AI4Science-WestlakeU

AI4Science-WestlakeU

README.md

AI for Scientific Simulation and Discovery Lab

Our research group at Westlake University (西湖大学) carries out long-term work on core and universal problems for AI + Science:

  • AI for scientific simulation, design, and control: Developing machine learning algorithms (based on Graph Neural Networks and Diffusion Models) for large-scale, multi-scale scientific simulation (applied to fluid dynamics, materials, plasmas), scientific design (protein design, materials design, mechanical design), and control (fluid control, PDE control);
  • AI for scientific discovery: Developing machine learning algorithms (based on foundation models and neuro-symbolic AI) to discover universal rules and internal structures in scientific systems (applied to life sciences and physics);

Group website: https://ai4s.lab.westlake.edu.cn/

Collaborators (a non-exhaustive list):

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  1. flow_guidance flow_guidance Public

    [ICML 2025] The official implementation of the paper "On the Guidance of Flow Matching"

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  2. diffphycon diffphycon Public

    [NeurIPS2024] DiffPhyCon uses generative models to control complex physical systems

    Jupyter Notebook 43 3

  3. wdno wdno Public

    [ICLR2025] Wavelet Diffusion Neural Operator (WDNO) uses diffusion models on wavelet space for generative PDE simulation and control.

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  4. cindm cindm Public

    [ICLR24] CinDM uses compositional generative models to design boundaries and initial states significantly more complex than the ones seen in training for physical simulation

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  5. beno beno Public

    [ICLR24] A boundary-embedded neural operator that incorporates complex boundary shape and inhomogeneous boundary values

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  6. t_scend t_scend Public

    This repo is the code for T-SCEND, a novel framework that significantly improves diffusion model’s reasoning capabilities with better energy-based training and scaling up test-time computation.

    Python 26 1

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