4 papers
Coupling Deep Learning with Full Waveform Inversion
Wen Ding, Kui Ren, Lu Zhang
Full waveform inversion (FWI) aims to reconstruct unknown physical coefficients in wave equations using the wavefield data generated from multiple incoming sources. In this work, w…
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…
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…
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…