What are the computational challenges of applying DMD to large-scale molecular dynamics simulations?

Aug 29, 2024 ... We apply here an unsupervised data-driven algorithm, Dynamic Mode Decomposition (DMD), to sEEG recordings from five focal epilepsy patients. Jul 28, 2023 ... When analyzing high-dimensional time series, the DMD algorithm requires extremely large amounts of computational power. To accelerate the DMD ... Nov 13, 2019 ... Semantic Scholar extracted view of "Dynamic mode decomposition based analysis of flow over a sinusoidally pitching airfoil" by Karthik Menon ... Abstract. Originally introduced in the fluid mechanics community, dynamic mode decomposition (DMD) has emerged as a powerful tool for analyzing the dynamics of ... The recently developed dynamic mode decomposition (DMD) is an innovative tool for integrating data with dynamical systems theory. The DMD has deep connections ... ... method for computing Dynamic Mode Decomposition (DMD) evolution matrices, which we use to analyze dynamical systems. Unlike the majority of existing methods ... PyDMD is a Python package that implements DMD and several of its major optimizations and methodological extensions. We develop an iterative approximation algorithm which couples the EDMD with a trainable dictionary represented by an artificial neural network. Mar 21, 2025 ... Using Dynamic Mode Decomposition with control (DMDc), we analyze surface air temperature patterns from climate simulations to elucidate the ... ... dynamic mode decomposition to reconstruct the final steady state given only few snapshots of the simulation, and the reduction of the parameter space by AS ...