dynamic mode decomposition
PulseAugur coverage of dynamic mode decomposition — every cluster mentioning dynamic mode decomposition across labs, papers, and developer communities, ranked by signal.
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NeuralDMD framework reconstructs spatio-temporal dynamics from sparse data
Researchers have introduced NeuralDMD, a novel framework that combines neural implicit representations with Dynamic Mode Decomposition (DMD) to reconstruct continuous spatio-temporal dynamics from sparse and noisy measu…
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New Koopman-based control method enhances turbofan engine performance
Researchers have developed a novel method for controlling turbofan engines using Koopman operator theory. This approach, detailed in a recent paper, utilizes an adapted dynamic mode decomposition to create a reusable Ko…
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New equivariant spectral submanifold reduction method speeds up complex modeling
Researchers have introduced equivariant spectral submanifold (eSSM) reduction, a new method for creating reliable nonlinear reduced-order models. This approach builds upon existing spectral submanifold (SSM) techniques …
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AI analyzes EEG signals to detect brain disorder dynamics
Researchers have developed a new method using Dynamic Mode Decomposition (DMD) to analyze high-frequency electroencephalography (EEG) signals for detecting brain disorder indicators. This technique identifies consistent…
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New method enhances equation discovery from noisy data using Koopman dynamics
Researchers have developed a dynamics-aware method for identifying governing equations from sparse and noisy data, building upon techniques like Sparse Identification of Nonlinear Dynamics (SINDy) and PDE Functional Ide…
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DeepCormack algorithms accelerate material Fermi surface studies
Researchers have developed DeepCormack, a novel set of data-driven algorithms designed to improve the reconstruction of 3D two-photon momentum density (TPMD) for material Fermi surface studies. This method integrates de…
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Scientific Machine Learning advances fluid dynamics simulation
A recent chapter reviews advancements in Scientific Machine Learning (SciML) for simulating complex fluid flow and transport phenomena. It highlights methods like Dynamic Mode Decomposition and Physics-Informed Neural N…
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Scientific Machine Learning advances fluid dynamics modeling · 2 sources tracked
This chapter explores advancements in Scientific Machine Learning (SciML) for simulating complex fluid flow and transport phenomena. It details methods like Singular Value Decomposition, Dynamic Mode Decomposition, Phys…
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New Deep Spectral Encoder Method for Dynamical Systems
Researchers have developed a novel spectral learning method called Deep Spectral Encoder (DSE) for analyzing stochastic nonlinear dynamical systems. DSE utilizes an operator-based latent state-space model where a neural…
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HiCache++ accelerates diffusion models using DMD and Prony basis
A new method called HiCache++ has been developed to significantly speed up diffusion models, such as those used in Stable Diffusion, without requiring additional training. This technique improves upon existing methods b…
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ViT depth computation approximated by linear dynamics
Researchers have explored the internal computations of Vision Transformers (ViTs) by applying Dynamic Mode Decomposition (DMD). Their findings suggest that contiguous blocks within a ViT can be approximated by a single …
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New CDM method enhances diffusion model distillation for faster, higher-fidelity image generation
Researchers have introduced Continuous-Time Distribution Matching (CDM), a novel method for accelerating diffusion models. This approach moves beyond discrete-time distillation by employing a dynamic, continuous schedul…
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Equation-free digital twins leverage Koopman theory for structural dynamics
Researchers have developed a new digital twin framework for monitoring complex engineering structures, particularly in challenging environments with non-stationary and nonlinear dynamics. This approach utilizes Koopman …