3 citations · 6 across the 5 of their papers we have counts for
6 papers
Cost-Accuracy Trade-offs: Neural Operator vs Classical Numerical Solver
Daniel Zhengyu Huang, Andrew M. Stuart
Neural operators are data-driven models that learn mappings from inputs that parameterize partial differential equations, such as spatially varying coefficients, initial conditions…
Optimal Experimental Design for Reliable Learning of History-Dependent Constitutive Laws
Kaushik Bhattacharya, Lianghao Cao, Andrew Stuart
History-dependent constitutive models serve as macroscopic closures for the aggregated effects of micromechanics. Their parameters are typically learned from experimental data. Wit…
Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient Flows
Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang +2
In this paper, we study efficient approximate sampling for probability distributions known up to normalization constants. We specifically focus on a problem class arising in Bayesi…
Gaussian Measures Conditioned on Nonlinear Observations: Consistency, MAP Estimators, and Simulation
Yifan Chen, Bamdad Hosseini, Houman Owhadi +1
The article presents a systematic study of the problem of conditioning a Gaussian random variable on nonlinear observations of the form where $ϕ: \mathcal{X} \to…
Sampling via Gradient Flows in the Space of Probability Measures
Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang +2
Sampling a target probability distribution with an unknown normalization constant is a fundamental challenge in computational science and engineering. Recent work shows that algori…
Diffusive optical tomography in the Bayesian framework
Kit Newton, Qin Li, Andrew Stuart
Many naturally-occuring models in the sciences are well-approximated by simplified models, using multiscale techniques. In such settings it is natural to ask about the relationship…