2023
DOI: 10.1016/j.softx.2023.101338
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j-Wave: An open-source differentiable wave simulator

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Cited by 10 publications
(7 citation statements)
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References 43 publications
(61 reference statements)
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“…To determine the impact of acoustic modeling, we ran the forward acoustic model in j-Wave 45 to calculate the pressure time series detected by the ultrasound transducer array for the initial pressure distributions. Simulations were run in three dimensions, with a grid size of 240 μm, and a speed of sound of 1500 m s1.…”
Section: Methodsmentioning
confidence: 99%
“…To determine the impact of acoustic modeling, we ran the forward acoustic model in j-Wave 45 to calculate the pressure time series detected by the ultrasound transducer array for the initial pressure distributions. Simulations were run in three dimensions, with a grid size of 240 μm, and a speed of sound of 1500 m s1.…”
Section: Methodsmentioning
confidence: 99%
“…Solvers for the two governing equations given in Section 2.1 are constructed using JaxDF [22]. This is a discretization framework that decouples the mathematical definition of the problem from the underlying discretization.…”
Section: Methodsmentioning
confidence: 99%
“…Here we present j-Wave: a customizable Python simulator, written on top of the JAX library [12] and the discretization framework JaxDF [22], for fast, parallelizable, and differentiable acoustic simulations. j-Wave solves both time-varying and timeharmonic forms of the wave equation with support for multiple discretizations, including finite differences and Fourier spectral methods, in 1D, 2D and 3D.…”
Section: Aimmentioning
confidence: 99%
“…It tests only the extremes of the HU-ρ functions within the ±1 STD ROI range. Fully sampling the HU-ρ distribution requires a Monte Carlo approach (Stanziola et al 2023b) or linear uncertainty propagation with a differentiable simulator (Stanziola et al 2023a(Stanziola et al , 2023b, which was beyond the scope of this work.…”
Section: Uncertaintymentioning
confidence: 99%