33 citations · 36 across the 8 of their papers we have counts for
8 papers
Adaptive design of experiment via normalizing flows for failure probability estimation
Hongji Wang, Tiexin Guo, Jinglai Li +1
Failure probability estimation problem is an crucial task in engineering. In this work we consider this problem in the situation that the underlying computer models are extremely e…
Active Learning for Saddle Point Calculation
Shuting Gu, Hongqiao Wang, Xiang Zhou
The saddle point (SP) calculation is a grand challenge for computationally intensive energy function in computational chemistry area, where the saddle point may represent the trans…
Inverse Gaussian Process regression for likelihood-free inference
Hongqiao Wang, Ziqiao Ao, Tengchao Yu +1
In this work we consider Bayesian inference problems with intractable likelihood functions. We present a method to compute an approximate of the posterior with a limited number of…
Inferring the unknown parameters in Differential Equation by Gaussian Process Regression with Constraint
Ying Zhou, Hongqiao Wang
Differential Equation (DE) is a commonly used modeling method in various scientific subjects such as finance and biology. The parameters in DE models often have interesting scienti…
Explicit Estimation of Derivatives from Data and Differential Equations by Gaussian Process Regression
Hongqiao Wang, Xiang Zhou
In this work, we employ the Bayesian inference framework to solve the problem of estimating the solution and particularly, its derivatives, which satisfy a known differential equat…
Maximum conditional entropy Hamiltonian Monte Carlo sampler
Tengchao Yu, Hongqiao Wang, Jinglai Li
The performance of Hamiltonian Monte Carlo (HMC) sampler depends critically on some algorithm parameters such as the total integration time and the numerical integration stepsize.…
Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions
Hongqiao Wang, Jinglai Li
We consider Bayesian inference problems with computationally intensive likelihood functions. We propose a Gaussian process (GP) based method to approximate the joint distribution o…
Gaussian process surrogates for failure detection: a Bayesian experimental design approach
Hongqiao Wang, Guang Lin, Jinglai Li
An important task of uncertainty quantification is to identify {the probability of} undesired events, in particular, system failures, caused by various sources of uncertainties. In…