Search Results for author:
Found 5 papers, 4 papers with code
A Learned Born Series for Highly-Scattering Media
A new method for solving the wave equation is presented, called the learned Born series (LBS), which is derived from a convergent Born Series but its components are found through training.
j-Wave: An open-source differentiable wave simulator
We present an open-source differentiable acoustic simulator, j-Wave, which can solve time-varying and time-harmonic acoustic problems.
Classical and learned MR to pseudo-CT mappings for accurate transcranial ultrasound simulation
Here, three methods for generating pseudo-CT images from magnetic resonance (MR) images were compared as an alternative to CT. A convolutional neural network (U-Net) was trained on paired MR-CT images to generate pseudo-CT images from either T1-weighted or zero-echo time (ZTE) MR images (denoted tCT and zCT, respectively).
A research framework for writing differentiable PDE discretizations in JAX
Differentiable simulators are an emerging concept with applications in several fields, from reinforcement learning to optimal control.
A Helmholtz equation solver using unsupervised learning: Application to transcranial ultrasound
Transcranial ultrasound therapy is increasingly used for the non-invasive treatment of brain disorders.
