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Analysis

This paper presents a novel semi-implicit variational multiscale (VMS) formulation for the incompressible Navier-Stokes equations. The key innovation is the use of an exact adjoint linearization of the convection term, which simplifies the VMS closure and avoids complex integrations by parts. This leads to a more efficient and robust numerical method, particularly in low-order FEM settings. The paper demonstrates significant speedups compared to fully implicit nonlinear formulations while maintaining accuracy, and validates the method on a range of benchmark problems.
Reference

The method is linear by construction, each time step requires only one linear solve. Across the benchmark suite, this reduces wall-clock time by $2$--$4\times$ relative to fully implicit nonlinear formulations while maintaining comparable accuracy.

Research#Control Systems🔬 ResearchAnalyzed: Jan 10, 2026 09:51

EBIF: A Novel Approach for Controlling Nonlinear Systems

Published:Dec 18, 2025 19:56
1 min read
ArXiv

Analysis

The article introduces EBIF, a novel control strategy based on exact bilinearization for control-affine nonlinear systems. This approach may offer improvements in stability and performance compared to traditional methods.
Reference

The article is sourced from ArXiv.

Research#llm📝 BlogAnalyzed: Jan 3, 2026 07:15

Interpolation, Extrapolation and Linearisation (Prof. Yann LeCun, Dr. Randall Balestriero)

Published:Jan 4, 2022 12:59
1 min read
ML Street Talk Pod

Analysis

This article discusses the concepts of interpolation, extrapolation, and linearization in the context of neural networks, particularly focusing on the perspective of Yann LeCun and his research. It highlights the argument that in high-dimensional spaces, neural networks primarily perform extrapolation rather than interpolation. The article references a paper by LeCun and others on this topic and suggests that this viewpoint has significantly impacted the understanding of neural network behavior. The structure of the podcast episode is also outlined, indicating the different segments dedicated to these concepts.
Reference

Yann LeCun thinks that it's specious to say neural network models are interpolating because in high dimensions, everything is extrapolation.