1 code implementation • 15 Apr 2024 • Daniel Zhengyu Huang, Jiaoyang Huang, Zhengjiang Lin
Score-based generative models have emerged as a powerful approach for sampling high-dimensional probability distributions.
1 code implementation • 8 Feb 2024 • Daniel Zhengyu Huang, Nicholas H. Nelsen, Margaret Trautner
Computationally efficient surrogates for parametrized physical models play a crucial role in science and engineering.
no code implementations • 5 Oct 2023 • Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M Stuart
Our third contribution is to study, and develop efficient algorithms based on Gaussian approximations of the gradient flows; this leads to an alternative to particle methods.
1 code implementation • 21 Feb 2023 • Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart
The flow in the Gaussian space may be understood as a Gaussian approximation of the flow.
1 code implementation • 14 Oct 2022 • Luning Sun, Daniel Zhengyu Huang, Hao Sun, Jian-Xun Wang
The equation residuals are used to inform the spline learning in a Bayesian manner, where approximate Bayesian uncertainty calibration techniques are employed to approximate posterior distributions of the trainable parameters.
5 code implementations • 11 Jul 2022 • Zongyi Li, Daniel Zhengyu Huang, Burigede Liu, Anima Anandkumar
The resulting geo-FNO model has both the computation efficiency of FFT and the flexibility of handling arbitrary geometries.
no code implementations • 19 Oct 2021 • Gérard Ben Arous, Daniel Zhengyu Huang, Jiaoyang Huang
In this paper, we consider the singular values and singular vectors of low rank perturbations of large rectangular random matrices, in the regime the matrix is "long": we allow the number of rows (columns) to grow polynomially in the number of columns (rows).