2 citations · 2 across the 5 of their papers we have counts for
11 papers
Deep regularization networks for inverse problems with noisy operators
Fatemeh Pourahmadian, Yang Xu
A supervised learning approach is proposed for regularization of large inverse problems where the main operator is built from noisy data. This is germane to superresolution imaging…
Laser Ultrasonic Imaging via the Time Domain Linear Sampling Method
Jian Song, Fatemeh Pourahmadian, Todd W. Murray +1
This study investigates the imaging ability of the time-domain linear sampling method (TLSM) when applied to laser ultrasonic (LU) tomography of subsurface defects from limited-ape…
Network scaling and scale-driven loss balancing for intelligent poroelastography
Yang Xu, Fatemeh Pourahmadian
A deep learning framework is developed for multiscale characterization of poroelastic media from full waveform data which is known as poroelastography. Special attention is paid to…
Ensemble WSINDy for Data Driven Discovery of Governing Equations from Laser-based Full-field Measurements
Abigail C. Schmid, Alireza Doostan, Fatemeh Pourahmadian
This work leverages laser vibrometry and the weak form of the sparse identification of nonlinear dynamics (WSINDy) for partial differential equations to learn macroscale governing…
Micropolar elastoplasticity using a fast Fourier transform-based solver
Noah M. Francis, Ricardo A. Lebensohn, Fatemeh Pourahmadian +1
This work presents a micromechanical spectral formulation for obtaining the full-field and homogenized response of elastoplastic micropolar composites. A closed-form radial-return…
Time- vs. frequency- domain inverse elastic scattering: Theory and experiment
Xiaoli Liu, Jian Song, Fatemeh Pourahmadian +1
This study formally adapts the time-domain linear sampling method (TLSM) for ultrasonic imaging of stationary and evolving fractures in safety-critical components. The TLSM indicat…