Flexible and Efficient Probabilistic PDE Solvers through Gaussian Markov Random Fields
Fast probabilistic counterparts to numerical PDE solvers, powered by SPDEs & sparse linear algebra.
tim@timwei.land
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Name
Status
PhD student ☕️
Affiliations
Methods of Machine Learning @ Uni Tübingen
Research
Scalable Probabilistic PDE solvers
Education
M.Sc. Machine Learning, University of Tübingen (2023)
Interests
Physics-informed ML, Bayesian Inference, ML Algorithms, Deep Learning
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tim@timwei.land:~$ news
[May 11 2026]
I will be co-presenting a tutorial on Probabilistic Numerics this July at ICML 2026 in Seoul: https://blog.icml.cc/2026/04/02/announcing-the-icml-2026-tutorials/
[Sep 15 2025]
I am visiting Toni Karvonen's lab in Lappeenranta for seven weeks.
[May 04 2025]
Attending AISTATS in Mai Khao to talk about our work on GMRFs for PDEs.
[Mar 01 2025]
I started my six month visit to Simo Särkkä's lab at Aalto University.
[Feb 21 2025]
Tobias Weber and I organized a seminar on Physics-Informed ML. Check out the results here: https://github.com/ClimatePDE/physics-informed-machine-learning
Featured Publications
Scaling up Probabilistic PDE Simulators with Structured Volumetric Information
Introduces GP-FVM, a probabilistic counterpart to the finite volume method, enabling large-scale probabilistic PDE simulations.
Featured Projects
GaussianMarkovRandomFields.jl
Fast, flexible and user-centered Julia package for Bayesian inference with sparse Gaussians
Scaling GP-based PDE Solvers
MSc Research Project. A case study on the shallow-water equations.
AI Exhibition Object on Decision Trees
Teaching people about machine learning by predicting apartment rents with decision trees.