5 papers
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…
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…
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…