Model Reduction and Surrogate Modeling (MORe 2026)

Politecnico di Milano, November 2-6, 2026.

This 5-day conference will bring together the international community of computational scientists, engineers, mathematicians, and domain experts from industry, the national laboratories and academia to address the topic of model reduction and surrogate modeling for high-dimensional complex systems. Model reduction and surrogate modeling is a field of research that combines applied and computational mathematics, computer science and many engineering domains. Approximating high-dimensional complex systems with low-dimensional efficient surrogate models requires rigorous mathematical analysis and algorithms. The issue of data sparsity and the multiscale nature of the data has been at the forefront of recent developments, while building on a 30-year history of increasingly complex reduced-model development. Moreover, methods that extrapolate well in time and when input parameter change, are of great interest to this community.

The conference merges activities of the two independent conference series MoRePaS and MODRED. The goal is to foster an international exchange of new concepts and ideas related to the following topics:

• Parametric model order reduction (MOR)
• System-theoretic model reduction methods and frequency-domain methods
• Machine learning and model order reduction (in particular when data is sparse)
• Non-intrusive and data driven approaches; hybrid data and physics based model reduction
• Tensor methods and kernel methods
• Nonlinear Model Reduction (e.g. geometric approaches on manifolds)
• MOR for problems with poor Kolmogorov N-width decay (e.g. transport phenomena, registration/OT methods)
• Localized MOR and multi-scale problems
• Randomized methods; MOR for uncertainty quantification
• Model reduction for optimization, control, inverse problems & data assimilation
• Dynamic, adaptive and on the fly reduced approximations, error estimation
• Structure-preserving and energy-based MOR (e.g. Hamiltonian or port-Hamiltonian systems)
• MOR for multiphysics/multiphase problems
• Model reduction for nonlinear bifurcating PDEs
• Model order reduction for predictive digital twins
• Model reduction software and benchmarks
• MOR for industrial applications and sustainable development