Efficient Modeling and Inference in Complex Systems

The field of complex systems modeling is moving towards the development of efficient and cost-effective methods for reducing the dimensionality of complex problems. Researchers are exploring new approaches to model reduction, inverse problems, and data assimilation, with a focus on balancing accuracy with computational efficiency. Notable advancements include the use of Bayesian active learning, dimension reduction techniques, and low-rank approximations to accelerate computations. These innovations have the potential to significantly impact various fields, including fluid dynamics, crisis management, and environmental monitoring. Noteworthy papers include:

  • The proposal of BayPOD-AL, an active learning framework for reduced-order modeling, which effectively reduces computational costs.
  • The development of a novel Ensemble Kalman Filter for data assimilation, which achieves significant reductions in computation time and RAM usage.
  • The introduction of a goal-oriented optimal sensor placement framework for PDE-constrained inverse problems, which enhances predictive accuracy in crisis management scenarios.

Sources

Cost-effective Reduced-Order Modeling via Bayesian Active Learning

Dimension and model reduction approaches for linear Bayesian inverse problems with rank-deficient prior covariances

Ensemble Kalman Filter for Data Assimilation coupled with low-resolution computations techniques applied in Fluid Dynamics

Goal-oriented optimal sensor placement for PDE-constrained inverse problems in crisis management

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