We present a novel deep learning framework for flow field predictions in irregular domains
when the solution is a function of the geometry of either the domain or objects inside the …

3D visual computing data are often spatially sparse. To exploit such sparsity, people have
developed hierarchical sparse data structures, such as multi-level sparse voxel grids …

Efficient simulation of the Navier-Stokes equations for fluid flow is a long standing problem in
applied mathematics, for which state-of-the-art methods require large compute resources. In …

We propose a method for the data‐driven inference of temporal evolutions of physical
functions with deep learning. More specifically, we target fluid flow problems, and we …

We present a novel data-driven algorithm to synthesize high resolution flow simulations with
reusable repositories of space-time flow data. In our work, we employ a descriptor learning …

Mean and turbulent properties of the wake generated by a single wind turbine are studied in
this paper with a new large eddy simulation (LES) code, the wind turbine and turbulence …

We present a new Eulerian fluid simulation method, which allows real-time simulations of
large scale three dimensional liquids. Such scenarios have hitherto been restricted to the …

Physically based fluid simulation requires much time in numerical calculation to solve Navier–
Stokes equations. Especially in grid‐based fluid simulation, because of iterative …

There are currently several communities working on algorithms for classification of high
dimensional data. This work develops a class of variational algorithms that combine recent …

We introduce a new method for fluid simulation on high-resolution adaptive grids which
rivals the throughput and parallelism potential of methods based on uniform grids. Our …