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

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  3. RealPDEBench RealPDEBench Public

    [ICLR26 Oral] RealPDEBench: A Benchmark for Complex Physical Systems with Paired Real-World and Simulated Data

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  5. diffphycon diffphycon Public

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

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  6. 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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