1 citations · 1 across the 6 of their papers we have counts for
6 papers
Nemytskii neural operator: a nonlinear model reduction method for parametrized partial differential equations
Jingye Li, Alex Bespalov, Jinglai Li
We introduce a Nemytskii neural operator framework for nonlinear model reduction of parametrized steady-state partial differential equations. The method generalizes reduced basis a…
Stochastic gradient descent based variational inference for infinite-dimensional inverse problems
Jiaming Sui, Junxiong Jia, Jinglai Li
This paper introduces two variational inference approaches for infinite-dimensional inverse problems, developed through gradient descent with a constant learning rate. The proposed…
A quantum gradient descent algorithm for optimizing Gaussian Process models
Junpeng Hu, Jinglai Li, Lei Zhang +1
Gaussian Process Regression (GPR) is a nonparametric supervised learning method, widely valued for its ability to quantify uncertainty. Despite its advantages and broad application…
Bayesian Optimization for CVaR-based portfolio optimization
Robert Millar, Jinglai Li
Optimal portfolio allocation is often formulated as a constrained risk problem, where one aims to minimize a risk measure subject to some performance constraints. This paper presen…
Gaussian Process Regression for Uncertainty Quantification: An Introductory Tutorial
Jinglai Li, Hongqiao Wang
Uncertainty Quantification (UQ) is essential for the reliable application of computational models in engineering and science. Among surrogate modeling techniques, Gaussian Process…
Model-Embedded Gaussian Process Regression for Parameter Estimation in Dynamical System
Ying Zhou, Jinglai Li, Xiang Zhou +1
Identifying dynamical system (DS) is a vital task in science and engineering. Traditional methods require numerous calls to the DS solver, rendering likelihood-based or least-squar…