Model Reduction and Surrogate Modeling (MORe2024)

9-13 Sep 2024 La Jolla, California (United States)


Photo by Erik Jepsen / UC San Diego


Welcome to the webpage of the Model Reduction and Surrogate Modeling 2024 (MORe24) Conference, which will be held at the Scripps Seaside Forum on the campus of the University of California San Diego, September 9-13, 2024. 

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

Previous MoRePaS editions were held in Münster (2009), Günzburg (2012), Trieste (2015) and Nantes (2018). Previous MODRED editions were held in Berlin (2010), Magdeburg (2013), Odense (2017), and Graz (2019). Previous MORe editions were held in Berlin (2022).

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