ProbNum 2026: The 2nd International Conference on Probabilistic Numerics
9–11 September 2026 in Lappeenranta, Finland
ProbNum 2026 is an international conference on Probabilistic Numerics, methods for statistically solving numerical problems (optimisation, integration, solving differential equations) and probabilistically quantifying the numerical errors as computational uncertainties. Probabilistic Numerics make numerical algorithms faster, more reliable, and easier to design and use. They are developed and used in machine learning, artificial intelligence, scientific simulation and computational statistics. More details can be found below.
ProbNum 2026 welcomes researchers and practitioners interested in methods, theory and applications of Probabilistic Numerics for an open exchange of ideas.
Venue, Dates and Registration
ProbNum 2026 will be held at the Lappeenranta campus of LUT University on 9–11 September 2026. The event is open to all.
Publications as PMLR Proceedings
- ProbNum 2026 calls for papers that will be published as Proceedings of Machine Learning Research (PMLR). The submission deadline will be in spring 2026.
- See the PMLR proceedings of ProbNum 2025 here.
Is ProbNum 2026 for me?
A probabilistic numerical method is an algorithm that quantifies errors arising from the finite nature of computation. In this context, the process of computation is often interpreted in the language of probabilistic (or Bayesian) inference, where computation is treated as a source of information, much like data in statistics and machine learning. Probabilistic numerical methods are thus also called computation aware. While such algorithms may in some cases use random numbers, stochasticity is a concept complementary to probabilistic inference: Probabilistic numerical methods are not necessarily stochastic numerical methods, and many stochastic numerical methods are not computation-aware. Instead, probabilistic numerical methods typically return probability measures, e.g. parametrized through sufficient statistics or moments. Beyond the basic goal of quantifying computational uncertainty, the value of probabilistic functionality is that it uses the same mathematical concepts and code functionality as statistics and machine learning, which simplifies overarching goals like inference on parameters or latent forces in scientific simulation (aka. inverse problems), joint inference on a physical system from both mechanistic and empirical data (aka. data assimilation), the adaptive control of algorithmic cost, or more generally the use of numerical methods inside of machine learning systems. In this sense, probabilistic numerical methods provide a native algorithmic formalism for AI, ML, and Statistics.
ProbNum 2026 aims to be an inclusive, enjoyable venue for the discussion, dissemination and promotion of such ideas. We welcome submissions from researchers in academia and industry, from theory to applications. We also invite interested participants who want to learn more about the field without submitting their own yet to attend.
Past Meetings
ProbNum 2026 is the latest edition of a series of events on Probabilistic Numerics since 2012. Past events can be found here.
Organisers
- Toni Karvonen (LUT University, Finland)
- Filip Tronarp (Lund University, Sweden)
- Wouter Kouw (TU Eindhoven, Netherlands)
Contact Information
Any questions can be sent to the organisers above, specifically as an email to probnum2026@gmail.com.