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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: 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) and scientific design (protein design, materials design, mechanical design);
  • AI for scientific discovery: Developing machine learning algorithms (based on neuro-symbolic AI and foundation models) to discover universal rules and internal structures in scientific systems (applied to life sciences and physics);

Website: https://tailin.org/.

Collaborators (a non-exhaustive list):

Popular repositories

  1. beno beno Public

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

    Python 17

  2. 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

    Jupyter Notebook 16 2

  3. le-pde-uq le-pde-uq Public

    [AAAI24] LE-PDE-UQ endows deep learning-based surrogate models with robust and efficient uncertainty quantification capabilities for both forward and inverse problems.

    Jupyter Notebook 5

  4. frontiers_in_AI_course frontiers_in_AI_course Public

    Jupyter Notebook 5

  5. .github .github Public

Repositories

Showing 5 of 5 repositories
  • 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

    Jupyter Notebook 16 MIT 2 0 0 Updated May 1, 2024
  • .github Public
    0 0 0 0 Updated Mar 5, 2024
  • frontiers_in_AI_course Public
    Jupyter Notebook 5 MIT 0 0 0 Updated Mar 4, 2024
  • le-pde-uq Public

    [AAAI24] LE-PDE-UQ endows deep learning-based surrogate models with robust and efficient uncertainty quantification capabilities for both forward and inverse problems.

    Jupyter Notebook 5 MIT 0 0 0 Updated Feb 26, 2024
  • beno Public

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

    Python 17 MIT 0 0 0 Updated Feb 22, 2024

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