2026-08-12 –, Room 2
ConvolutionInterpolations.jl offers methods for high-order interpolation, differentiation, integration and smoothing on discrete grids. Query times are similar to those of popular Julia packages for interpolation. Grids can be uniform, non-uniform, or a mixture. Supports mixed per-dimension interpolation, differentiation and integration. Extends naturally to multi-dimensional applications.
This talk will be a practical demonstration of the features of ConvolutionInterpolations.jl.
After a brief introduction of the package and its author, this talk will proceed live in the Julia REPL.
Features which will be demonstrated include:
- A selection of the available interpolation methods, from simple to high accuracy.
- Performance comparison with other interpolation packages.
- Non-uniform grids.
- High-order smooth derivatives from discrete samples.
- Integrals.
- Dimension-wise kernel and derivative selections.
- Multi-dimensional application (2D).
Future plans include: a Python port, writing a paper, PDE applications, more documentation.
Attendees will leave with the intuition of how, when and why to use ConvolutionInterpolations.jl.
I work as an engineer with model-based energy planning. In my sparetime I enjoy researching, developing and programming in Julia.