66 citations · 358 across the 29 of their papers we have counts for
33 papers
Recovering coefficients in a system of semilinear Helmholtz equations from internal data
Kui Ren, Nathan Soedjak
We study an inverse problem for a coupled system of semilinear Helmholtz equations where we are interested in reconstructing multiple coefficients in the system from internal data…
Masked Diffusion Models Are Fast Distribution Learners
Jiachen Lei, Qinglong Wang, Peng Cheng +5
Diffusion model has emerged as the \emph{de-facto} model for image generation, yet the heavy training overhead hinders its broader adoption in the research community. We observe th…
Adaptive State-Dependent Diffusion for Derivative-Free Optimization
Björn Engquist, Kui Ren, Yunan Yang
This paper develops and analyzes a stochastic derivative-free optimization strategy. A key feature is the state-dependent adaptive variance. We prove global convergence in probabil…
Transport models for wave propagation in scattering media with nonlinear absorption
Joseph Kraisler, Wei Li, Kui Ren +2
This work considers the propagation of high-frequency waves in highly-scattering media where physical absorption of a nonlinear nature occurs. Using the classical tools of the Wign…
A Model-Consistent Data-Driven Computational Strategy for PDE Joint Inversion Problems
Kui Ren, Lu Zhang
The task of simultaneously reconstructing multiple physical coefficients in partial differential equations (PDEs) from observed data is ubiquitous in applications. In this work, we…
An Algebraically Converging Stochastic Gradient Descent Algorithm for Global Optimization
Björn Engquist, Kui Ren, Yunan Yang
We propose a new gradient descent algorithm with added stochastic terms for finding the global optimizers of nonconvex optimization problems. A key component in the algorithm is th…