306 citations · 668 across the 30 of their papers we have counts for
22 papers · 1 filter
ROMNet: a hybrid reduced order modeling and machine learning approach to waveform inversion
Liliana Borcea, Alexander Mamonov, Kui Ren +2
Waveform inversion seeks to estimate the wave speed of a heterogeneous, inaccessible medium, from time-resolved measurements of the waves at user controlled sensors. We consider th…
Quantitative synthetic aperture radar inversion
Liliana Borcea, Josselin Garnier, Alexander V. Mamonov +1
We study an inverse scattering problem for monostatic synthetic aperture radar (SAR): Estimate the wave speed in a heterogeneous, isotropic and nonmagnetic medium probed by waves e…
Reduced Order Modeling for First Order Hyperbolic Systems with Application to Multiparameter Acoustic Waveform Inversion
Liliana Borcea, Josselin Garnier, Alexander V. Mamonov +1
Waveform inversion seeks to estimate an inaccessible heterogeneous medium from data gathered by sensors that emit probing signals and measure the generated waves. It is an inverse…
Inverse scattering for Schrödinger equation in the frequency domain via data-driven reduced order modeling
Andreas Tataris, Tristan van Leeuwen, Alexander V. Mamonov
In this paper we develop a numerical method for solving an inverse scattering problem of estimating the scattering potential in a Schrödinger equation from frequency domain measure…
A priori analysis of a tensor ROM for parameter dependent parabolic problems
Alexander V. Mamonov, Maxim A. Olshanskii
A space-time-parameters structure of parametric parabolic PDEs motivates the application of tensor methods to define reduced order models (ROMs). Within a tensor-based ROM framewor…
Tensorial parametric model order reduction of nonlinear dynamical systems
Alexander V. Mamonov, Maxim A. Olshanskii
For a nonlinear dynamical system that depends on parameters, the paper introduces a novel tensorial reduced-order model (TROM). The reduced model is projection-based, and for syste…
When data driven reduced order modeling meets full waveform inversion
Liliana Borcea, Josselin Garnier, Alexander V. Mamonov +1
Waveform inversion is concerned with estimating a heterogeneous medium, modeled by variable coefficients of wave equations, using sources that emit probing signals and receivers th…
Velocity estimation via model order reduction
Alexander V. Mamonov, Liliana Borcea, Josselin Garnier +1
A novel approach to full waveform inversion (FWI), based on a data driven reduced order model (ROM) of the wave equation operator is introduced. The unknown medium is probed with p…
Waveform inversion with a data driven estimate of the internal wave
Liliana Borcea, Josselin Garnier, Alexander V. Mamonov +1
We study an inverse problem for the wave equation, concerned with estimating the wave speed, aka velocity, from data gathered by an array of sources and receivers that emit probing…
Waveform inversion via reduced order modeling
Liliana Borcea, Josselin Garnier, Alexander V. Mamonov +1
We introduce a novel approach to waveform inversion, based on a data driven reduced order model (ROM) of the wave operator. The presentation is for the acoustic wave equation, but…
Interpolatory tensorial reduced order models for parametric dynamical systems
Alexander V. Mamonov, Maxim A. Olshanskii
The paper introduces a reduced order model (ROM) for numerical integration of a dynamical system which depends on multiple parameters. The ROM is a projection of the dynamical syst…
Reduced Order Model Approach to Inverse Scattering
Liliana Borcea, Vladimir Druskin, Alexander V. Mamonov +2
We study an inverse scattering problem for a generic hyperbolic system of equations with an unknown coefficient called the reflectivity. The solution of the system models waves (so…
Reduced order models for spectral domain inversion: Embedding into the continuous problem and generation of internal data
Liliana Borcea, Vladimir Druskin, Alexander V. Mamonov +2
We generate data-driven reduced order models (ROMs) for inversion of the one and two dimensional Schrödinger equation in the spectral domain given boundary data at a few frequencie…
Robust nonlinear processing of active array data in inverse scattering via truncated reduced order models
Liliana Borcea, Vladimir Druskin, Alexander V. Mamonov +1
We introduce a novel algorithm for nonlinear processing of data gathered by an active array of sensors which probes a medium with pulses and measures the resulting waves. The algor…
A nonlinear method for imaging with acoustic waves via reduced order model backprojection
Vladimir Druskin, Alexander V. Mamonov, Mikhail Zaslavsky
We introduce a novel nonlinear imaging method for the acoustic wave equation based on data-driven model order reduction. The objective is to image the discontinuities of the acoust…
Second-Harmonic Imaging in Random Media
Liliana Borcea, Wei Li, Alexander V. Mamonov +1
We consider the problem of optical imaging of small nonlinear scatterers in random media. We propose an extension of coherent interferometric imaging (CINT) that applies to scatter…
Multi-scale S-fraction reduced-order models for massive wavefield simulations
Vladimir Druskin, Alexander V. Mamonov, Mikhail Zaslavsky
We developed a novel reduced-order multi-scale method for solving large time-domain wavefield simulation problems. Our algorithm consists of two main stages. During the first "off-…
A discrete Liouville identity for numerical reconstruction of Schrödinger potentials
Liliana Borcea, Fernando Guevara Vasquez, Alexander V. Mamonov
We propose a discrete approach for solving an inverse problem for Schrödinger's equation in two dimensions, where the unknown potential is to be determined from boundary measuremen…
Direct, nonlinear inversion algorithm for hyperbolic problems via projection-based model reduction
Vladimir Druskin, Alexander Mamonov, Andrew E. Thaler +1
We estimate the wave speed in the acoustic wave equation from boundary measurements by constructing a reduced-order model (ROM) matching discrete time-domain data. The state-variab…
Nonlinear seismic imaging via reduced order model backprojection
Alexander V. Mamonov, Vladimir Druskin, Mikhail Zaslavsky
We introduce a novel nonlinear seismic imaging method based on model order reduction. The reduced order model (ROM) is an orthogonal projection of the wave equation propagator oper…
S-fraction multiscale finite-volume method for spectrally accurate wave propagation
Vladimir Druskin, Alexander V. Mamonov, Mikhail Zaslavsky
We develop a method for numerical time-domain wave propagation based on the model order reduction approach. The method is built with high-performance computing (HPC) implementation…
A model reduction approach to numerical inversion for a parabolic partial differential equation
Liliana Borcea, Vladimir Druskin, Alexander V. Mamonov +1
We propose a novel numerical inversion algorithm for the coefficients of parabolic partial differential equations, based on model reduction. The study is motivated by the applicati…