30 citations · 74 across the 8 of their papers we have counts for
8 papers
Synergizing chemical and AI communities for advancing laboratories of the future
Saejin Oh, Xinyi Fang, I-Hsin Lin +6
The development of automated experimental facilities and the digitization of experimental data have introduced numerous opportunities to radically advance chemical laboratories. As…
Fast phase prediction of charged polymer blends by white-box machine learning surrogates
Clayton Ellis, Xinyi Fang, Christopher Balzer +4
Compatibilized polymer blends are a complex, yet versatile and widespread category of material. When the components of a binary blend are immiscible, they are typically driven towa…
Neural Operators for Forward and Inverse Potential-Density Mappings in Classical Density Functional Theory
Runtong Pan, Xinyi Fang, Kamyar Azizzadenesheli +3
Neural operators are capable of capturing nonlinear mappings between infinite-dimensional functional spaces, offering a data-driven approach to modeling complex functional relation…
The inverse Kalman filter
Xinyi Fang, Mengyang Gu
We introduce the inverse Kalman filter, which enables exact matrix-vector multiplication between a covariance matrix from a dynamic linear model and any real-valued vector with lin…
Data-driven model construction for anisotropic dynamics of active matter
Mengyang Gu, Xinyi Fang, Yimin Luo
The dynamics of cellular pattern formation is crucial for understanding embryonic development and tissue morphogenesis. Recent studies have shown that human dermal fibroblasts cult…
Molecular-scale substrate anisotropy and crowding drive long-range nematic order of cell monolayers
Yimin Luo, Mengyang Gu, Minwook Park +7
The ability of cells to reorganize in response to external stimuli is important in areas ranging from morphogenesis to tissue engineering. Elongated cells can co-align due to steri…
Reliable emulation of complex functionals by active learning with error control
Xinyi Fang, Mengyang Gu, Jianzhong Wu
A statistical emulator can be used as a surrogate of complex physics-based calculations to drastically reduce the computational cost. Its successful implementation hinges on an acc…
Scalable marginalization of correlated latent variables with applications to learning particle interaction kernels
Mengyang Gu, Xubo Liu, Xinyi Fang +1
Marginalization of latent variables or nuisance parameters is a fundamental aspect of Bayesian inference and uncertainty quantification. In this work, we focus on scalable marginal…