Name: Data-driven methods for partial differential equations
Start: 2026-03-02T10:00:00+01:00
End: 2026-03-04T18:30:00+01:00
Location: Karlsruhe Institute of Technology

Data-driven methods for partial differential equations

from Monday, March 2, 2026 (10:00 AM) to Wednesday, March 4, 2026 (6:30 PM)

Monday, March 2, 2026

10:00 AM Registration: Registration
Registration
10:00 AM - 1:00 PM
Room: Room 1.067

1:00 PM Opening: Opening
Opening
1:00 PM - 1:15 PM
1:15 PM Invited talks 1
1:15 PM - 2:45 PM
Contributions

1:15 PM AI for Efficient Simulations of PDEs - Siddhartha Mishra (ETH Zurich)

2:00 PM Solving time-dependent PDEs with random feature models - Felix Dietrich
2:45 PM Coffee break
Coffee break
2:45 PM - 3:15 PM
3:15 PM Invited talks 2
3:15 PM - 4:45 PM
Contributions

3:15 PM A statistical tour of physics-informed learning - Claire Boyer (Université Paris-Saclay)

4:00 PM When do World Models Successfully Learn Dynamic Systems? - Hanno Gottschalk (TU Berlin)
5:15 PM Panel Discussion

5:15 PM - 6:15 PM
6:15 PM Poster blitz, poster session, and welcome reception
6:15 PM - 9:15 PM
Contributions

6:15 PM Neural semi-Lagrangian method for high-dimensional advection-diffusion problems - Laurent Navoret (University of Strasbourg, Inria)

6:18 PM Prediction error certification for PINNs: Theory, computation and application - Birgit Hillebrecht (KIT)

6:21 PM Posterior contraction under misspecification and heteroskedasticity in non-linear inverse problems - Maximilian Siebel (Heidelberg University)

6:24 PM Geometric Optimization in Scientific Machine Learning - Johannes Müller

6:27 PM Hyperbolic Traffic Models with Uncertain Accident Dynamics - Thomas Schillinger (University of Mannheim)

6:30 PM Exploratory computation of statistical Navier–Stokes solutions - Stephan Simonis (ETH Zurich)
Tuesday, March 3, 2026
8:30 AM Invited talks 3
8:30 AM - 10:00 AM
Contributions

8:30 AM Finite-precision arithmetic in deep learning for scientific computing - Philipp Petersen (Universität Wien)

9:15 AM Optimal Sensor Placement for Linear Inverse Problems via Measure Optimisation - Andrew Duncan (Imperial College London)
10:00 AM Coffee break
Coffee break
10:00 AM - 10:30 AM
10:30 AM Invited talks 4
10:30 AM - 12:00 PM
Contributions

10:30 AM Structure-preserving model reduction: From the formulation on manifolds to data-driven realizations - Silke Glas (University of Twente)

11:15 AM Data-driven reduced modeling of chaotic, turbulent, and stochastic systems via population dynamics - Benjamin Peherstorfer (Courant Institute of Mathematical Sciences, New York University)
12:00 PM Lunch break
Lunch break
12:00 PM - 2:00 PM
2:00 PM Invited talks 5
2:00 PM - 3:30 PM
Contributions

2:00 PM Ensemble Kalman Methods for Optimization: Subspace Control, Subsampling, and Applications in Machine Learning and Optimal Control - Claudia Schillings (FU Berlin)

2:45 PM Data assimilation with dissipative nonlinear dynamical systems: Optimal Gaussian asymptotics for the posterior measure - Dimitri Konen (University of Cambridge)
3:30 PM Coffee break
Coffee break
3:30 PM - 4:00 PM
4:00 PM Invited talks 6
4:00 PM - 5:30 PM
Contributions

4:00 PM Learning stochastic reduced order models from data - Melina Freitag (University of Potsdam)

4:45 PM Statistical Guarantees for Denoising Reflected Diffusion Models - Claudia Strauch
7:00 PM Conference Dinner
Conference Dinner
7:00 PM - 9:30 PM
Wednesday, March 4, 2026
8:30 AM Invited talks 7
8:30 AM - 10:00 AM
Contributions

8:30 AM On gradient stability in nonlinear PDE models and inference in interacting particle systems - Aurélien Castre (University of Cambridge, DPMMS)

9:15 AM Data-Driven Methods to Solve Maxwell's equations - Carsten Rockstuhl
10:00 AM Closing

10:00 AM - 10:30 AM

Choose timezone