What are the challenges of applying DMD to large-scale and noisy brain imaging data?

In this paper, we will propose an extension of DMD that exploits low-rank tensor decompositions of potentially high-dimensional data sets. Mar 1, 2023 ... Abstract. In this work, we demonstrate how physical principles—such as symmetries, invariances and conservation laws—can be integrated into the ... Feb 8, 2025 ... Huang, Qiuxiang, Kou, Jiaqing, Tian, Fang Bao, Young, John, Lai, Joseph C.S., & Ravi, Sridhar (2023) Dynamic mode decomposition for unsteady ... Apr 6, 2017 ... ... dynamic mode decomposition (WR-DMD). Our proposed method is validated on ten different healthy volunteers' kidney DCE-MRI data sets. The ... Jun 19, 2019 ... ... method for modeling dynamical systems called the Dynamic Mode Decomposition (DMD). This is a recent method that has first emerged in the ... Jul 28, 2023 ... When analyzing high-dimensional time series, the DMD algorithm requires extremely large amounts of computational power. To accelerate the DMD ... Jan 20, 2021 ... Abstract. Fluctuating surface pressures on a bluff body exposed to a boundary layer flow generally are characterized as a spatiotemporally ... Abstract. This work develops compressed sensing strategies for computing the dynamic mode decomposition (DMD) from heavily subsampled or compressed data. The ... Jul 20, 2024 ... ... dynamic mode decomposition method'. Together they form a unique fingerprint. Decomposition Method Mathematics 100%. Dynamic Mode ... Oct 5, 2024 ... Keyword(s): Dynamic Mode Decomposition, Flow modeling and control, Convex optimization, Sparsity. [bibtex-entry]. Conference articles. E ...