We present TSR-VFD, a novel deep learning solution that recovers temporal super-
resolution (TSR) of three-dimensional vector field data (VFD) for unsteady flow. In scientific …

We present a new approach for streamline-based flow field representation and reduction.
Our method can work in the in situ visualization setting by tracing streamlines from each time …

Vector fields analysis traditionally distinguishes conservative (curl-free) from mass
preserving (divergence-free) components. The Helmholtz-Hodge decomposition allows …

Implicit surface reconstruction from unorganized point sets has been recently approached
with methods based on multi-level partition of unity. We improve this approach by …

Vector field is mostly linearly approximated for the purpose of classification and description.
This approximation gives us only basic information of the vector field. We will show how to …

Recent developments in data acquisition technology enable to directly capture real vector
fields, helping for a better understanding of physical phenomena. However measured data …

The vector fields may be results from the measurements of real flow experiments. However,
during the measurements, some parts of the vector field can be measured incorrectly or …

We consider the problem of surface reconstruction of a geometric object from a finite set of
sample points with normals. Our contribution is to present a new scheme for implicit surface …

We study a spline-based approximation of vector fields in the conservative case (the
gradient vector field derives from a potential function). We introduce a minimization problem …

We present a mathematical model to reconstruct vector fields from given sparse samples
inside the domain. We applied the presented model to reconstruct the velocity vector field …