2026 NDNS+ yearly meeting

2026 NDNS+ yearly meeting

Location

The 2026 NDNS+ yearly meeting will be a one-day, hosted by ACDC, and funded by NDNS+. It will take place on the 13th of March 2026 in room NU-4A25 of the NU building (see here for directions) from 10:15 to 19:00.

Registration and poster submission

Registration for the event is mandatory and will cost 25Euros (a form will be circulated in due course).

Talks and posters

This year's event is centred around the cluster's Special Activity Groups (SAGs). We have asked SAGs to introduce themselves and their activities, and to follow up with a thematic talk. Our SAGs are:

We solicit the submission posters, which will be possible in due course using the registration form.

Schedule

Time Activity
10:15-11:00 Arrival, registration, coffee
11:00-11:05 Opening
11:05-11:40 SAG talks: Analysis of Partial Differential Equations
11:40-12:15 SAG talks: Structure-Preserving numerical methods
12:15-13:45 Lunch and posters
13:45-14:20 SAG talks: Uncertainty Quantification
14:20-14:55 SAG talks: Calculus of Variations
14:55-15:30 SAG talks: Inverse Problems
15:30-16:00 Coffee/tea break
16:00-17:45 Invited talks and NDNS+ discussion
17:45-19:00 Drinks

Titles and abstracts of the lectures will be included here in due course

Speaker for SAG Structure-Preserving Numerical Methods: Tom Tyranowski (Twente)

Title: Learning deterministic and stochastic forced Hamiltonian systems

Abstract: We present a neural network architecture capable of learning the parameter-dependent flow of a Hamiltonian system subject to external forcing, while preserving the underlying Lagrange-d’Alembert structure. We demonstrate that this architecture can learn the flows of time-dependent systems—both deterministic and stochastic—and more accurately emulate the system’s energy evolution compared to general-purpose, non-structure-preserving neural networks. This results in more stable and higher-quality solutions. We also discuss prospective applications to structure-preserving model reduction of stochastic Hamiltonian systems.

Speaker for SAG Uncertainty Quantification: Wouter Edeling (CWI)

Title: Deep Active Subspaces for High-Dimensional Uncertainty Quantification

Abstract: The deep active subspace method is a neural-network based tool for the propagation of uncertainty through computational models with high-dimensional input spaces. Unlike the original active subspace method, it does not require access to the gradient of the model. It relies on an orthogonal projection matrix constructed with Gram-Schmidt orthogonalization, which is incorporated as a linear encoder into a neural network, and optimized using back propagation to identify the latent variables, i.e. the active subspace of the input. We assess the performance of the deep active subspace method on epidemiological models and molecular dynamics with a particular focus on uncertainties in the high-dimensional force-field parameters, which affect key quantities of interest, including macroscopic material properties and binding free energy predictions in drug discovery. We also discuss its potential for constructing efficient priors for uncertainty quantification in neural network weights.

Organizers