*Attendance for this event is by invitation only*
This workshop will be held at the Alan Turing Institute, London, England.
The accuracy and robustness of numerical predictions that are based on mathematical models depend critically upon the construction of accurate discrete approximations to key quantities of interest. The exact error due to approximation will be unknown to the analyst, but worst-case upper bounds can often be obtained. This working group aims, instead, to develop Probabilistic Numerical Methods, which provide the analyst with a richer, probabilistic quantification of the numerical error in their output, thus providing better tools for reliable statistical inference.
The goals of the workshop include the presentation and critical discussion of WG research results, strategic planning of future activities and addressing grand challenges.
SAMSI-supported participants for this event are:
- Oksana Chkrebtii (Ohio State)
- Houman Owhadi (Caltech)
- Florian Schäefer (Caltech)
- Youseff Marzouk (MIT)
- Alessio Spantini (MIT)
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