Yixuan Wang

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G3 PhD candidate,
Applied + Computational Mathematics,
California Institute of Technology (Caltech)
E-mail: roywang [@] caltech [dot] edu.

About Me

I received a B.S. degree in mathematics summa cum laude from Peking University (PKU), in 2020. My undergraduate supervisor is Prof. Ruo Li.
My graduate supervisor is Prof. Thomas Yizhao Hou. I also work with Prof. Andrew Stuart and Prof. Anima Anandkumar. Check out my candidacy slides.

Research

My research interests broadly lie in

  • Numerical Analysis

  • Partial Differential Equation

  • Applied Probability

  • AI for Science

Actively looking for discussions and possible collaborations on interesting topics.

Find out more

Publications

  1. R. Li, Y.L Wang and Y.X. Wang. Approximation to Singular Quadratic Collision Model in Fokker-Planck-Landau Equation,
    SIAM Journal on Scientific Computing, 42(3), 2020, pp. B792-B815. [paper, slides]

  2. Z. Liu, S. Qian, Y. Wang, Y. Yan and T Yang. Schrödinger Principal-component Analysis: On the Duality between Principal-component Analysis and the Schrödinger Equation,
    Physical Review E, 104(2), 2021, 025307. [paper, slides]

  3. Y. Chen, T.Y. Hou and Y. Wang. Exponential Convergence for Multiscale Linear Elliptic PDEs via Adaptive Edge Basis Functions,
    Multiscale Modeling and Simulation, 19(2), 2021, pp. 980–1010. [paper]

  4. Y. Chen, T.Y. Hou and Y. Wang. Exponentially Convergent Multiscale Methods for 2D High Frequency Heterogeneous Helmholtz Equations,
    Multiscale Modeling and Simulation, 21(3), 2023, pp. 849–883. [paper, slides]

  5. Y. Chen, T.Y. Hou and Y. Wang. Exponentially Convergent Multiscale Finite Element Method,
    Communications on Applied Mathematics and Computation, 1-17, 2023. [paper, slides, poster]

  6. Z. Liu, A. Stuart and Y. Wang. (2022) Second Order Ensemble Langevin Method for Sampling and Inverse Problems,
    [arxiv, slides]

  7. H. Maust, Z. Li, Y. Wang, D. Leibovici, O. Bruno, T.Y. Hou and A. Anandkumar. Fourier Continuation for Exact Derivative Computation in Physics-Informed Neural Operators,
    NeurIPS 2022, 3rd AI for Science workshop. [arxiv, poster]

  8. T.Y. Hou and Y. Wang. (2023) Blowup Analysis for a Quasi-exact 1D Model of 3D Euler and Navier-Stokes,
    [arxiv, slides]

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