MAKING SCIENCE COMPUTABLE

Five overlapping translucent ellipses in pastel purple, blue, pink, orange and yellow, like a Venn diagram, their overlaps in mixed tones.
  • Physics
  • Machine Learning
  • Computational Science
  • Mathematics
  • Informed Machine Learning
  • Machine Learning for Scientific Computing
  • Algorithmics
  • Point Cloud Simulation
  • Learning-Based Numerical Methods
  • Sbalzarini Lab

We derive results from data through machine learning and from governing equations through numerical simulation.

Latest News

  1. New preprint on comparing dynamic shapes

    The preprint introduces the push-forward transform, a continuous and robust way to compare shapes that move and deform over time.

    Read the preprint

    A disc and the same disc deformed into three lobes, both drawn as clouds of dots.
  2. Open PhD positions in scientific machine learning

    We are looking for doctoral students who want to combine numerical analysis with machine learning. Get in touch with a short note on what you would like to work on.

    Get in touch

  3. New members join the group

    Two doctoral students joined the group in May, one working on particle methods and one on scientific machine learning. Welcome!

    Meet them

All news

Recent Publications

  1. Solving the Incompressible Navier-Stokes Equations on Oriented Curved Surfaces Discretized by Point Clouds

    A. Foggia and I. F. Sbalzarini

    arXiv preprint arXiv:2609.00216, 2026

  2. Spatiotemporal Control of Charge +1 Topological Defects in Polar Active Matter

    B. C. Geerds, A. Singh, M. Dedenon, D. J. G. Pearce, F. Jülicher, I. F. Sbalzarini and K. Kruse

    Phys. Rev. Res 8(3):033155, 2026

  3. Multivariate Newton interpolation in downward closed spaces reaches the optimal Bernstein–Walsh approximation rate

    M. Hecht, P. A. Hofmann, D. Wicaksono, U. Hernandez Acosta, K. Gonciarz, J. Kissinger, V. Sivkin and I. F. Sbalzarini

    IMA Journal of Numerical Analysis, draf137, 2026

All publications