Public articles linked to the same research event.
arXiv Spinverse presents a permeability-aware microstructure reconstruction method: on a fixed tetrahedral grid it treats each interior face permeability as a learnable parameter, optimizes face permeabilities by backpropagating a signal-matching loss through a fully differentiable Bloch-Torrey simulator, and recovers an interface by thresholding the learned permeability field; across a collection of synthetic voxel meshes it reconstructs diverse geometries and shows that sequence scheduling and regularization are critical to avoid outline-only solutions while improving boundary accuracy and structural validity.
Spinverse presents a permeability-aware microstructure reconstruction method: on a fixed tetrahedral grid it treats each interior face permeability as a learnable parameter, optimizes face permeabilities by backpropagating a signal-matching loss through a fully differentiable Bloch-Torrey simulator, and recovers an interface by thresholding the learned permeability field; across a collection of synthetic voxel meshes it reconstructs diverse geometries and shows that sequence scheduling and regularization are critical to avoid outline-only solutions while improving boundary accuracy and structural validity.
Spinverse presents a permeability-aware microstructure reconstruction method: on a fixed tetrahedral grid it treats each interior face permeability as a learnable parameter, optimizes face permeabilities by backpropagating a signal-matching loss through a fully differentiable Bloch-Torrey simulator, and recovers an interface by thresholding the learned permeability field; across a collection of synthetic voxel meshes it reconstructs diverse geometries and shows that sequence scheduling and regularization are critical to avoid outline-only solutions while improving boundary accuracy and structural validity.
Spinverse presents a permeability-aware microstructure reconstruction method: on a fixed tetrahedral grid it treats each interior face permeability as a learnable parameter, optimizes face permeabilities by backpropagating a signal-matching loss through a fully differentiable Bloch-Torrey simulator, and recovers an interface by thresholding the learned permeability field; across a collection of synthetic voxel meshes it reconstructs diverse geometries and shows that sequence scheduling and regularization are critical to avoid outline-only solutions while improving boundary accuracy and structural validity.