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Stochastic Parameterization with Dynamic Mode Decomposition
A physical stochastic parameterization is adopted in this work to account for the effects of the unresolved small-scale on the large-scale flow...
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A data-driven surrogate modeling approach for time-dependent incompressible Navier-Stokes equations with dynamic mode decomposition and manifold interpolation
This work introduces a novel approach for data-driven model reduction of time-dependent parametric partial differential equations. Using a multi-step...
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A Dynamic Mode Decomposition Based Reduced-Order Model For Parameterized Time-Dependent Partial Differential Equations
We propose a reduced-order model (ROM) based on dynamic mode decomposition (DMD) for efficient reduced-order modeling of parameterized time-dependent...
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Strong consistency of the projected total least squares dynamic mode decomposition for datasets with random noise
Dynamic mode decomposition (DMD) has attracted much attention in recent years as an analysis method for time series data. In this paper, we perform...
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An Adaptive Data-Driven Reduced Order Model Based on Higher Order Dynamic Mode Decomposition
A new data-driven reduced order model is developed to efficiently simulate transient dynamics, with the aim at computing the final attractor. The...
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Low-Rank Dynamic Mode Decomposition: An Exact and Tractable Solution
This work studies the linear approximation of high-dimensional dynamical systems using low-rank dynamic mode decomposition. Searching this...
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A Data-Driven Partitioned Approach for the Resolution of Time-Dependent Optimal Control Problems with Dynamic Mode Decomposition
This work recasts time-dependent optimal control problems governed by partial differential equations in a Dynamic Mode Decomposition with control...
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Dynamic Mode Decomposition for Continuous Time Systems with the Liouville Operator
Dynamic mode decomposition (DMD) has become synonymous with the Koopman operator, where continuous time dynamics are discretized and examined using...
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Two-Stage Dynamic Programming in the Routing Problem with Decomposition
AbstractThis paper considers an optimal movement routing problem with constraints. One such constraint is due to decomposing the original problem...
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Toward Fitting Structured Nonlinear Systems by Means of Dynamic Mode Decomposition
The dynamic mode decomposition (DMD) is a data-driven method used for identifying the dynamics of complex nonlinear systems. It extracts important...
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Data Driven Stochastic Primitive Equations with Dynamic Modes Decomposition
As planetary flows are characterised by interaction of phenomenons in a huge range of scales, it is unaffordable today to resolve numerically the...
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Deflated domain decomposition method for structural problems
The paper presents a fast and stable solver algorithm for structural problems. The point is the distance between the eigenvector of the constrained...
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A Randomized Singular Value Decomposition for Third-Order Oriented Tensors
The oriented singular value decomposition (O-SVD) proposed by Zeng and Ng provides a hybrid approach to the t-product-based third-order tensor...
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Dynamic Mode Decomposition: A Tool to Extract Structures Hidden in Massive Datasets
Dynamic Mode Decomposition (DMD) is able to decompose flow field data into coherent modes and determine their oscillatory frequencies and...
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On Large-Scale Dynamic Topic Modeling with Nonnegative CP Tensor Decomposition
There is currently an unprecedented demand for large-scale temporal data analysis due to the explosive growth of data. Dynamic topic modeling has...
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Learning Proper Orthogonal Decomposition of Complex Dynamics Using Heavy-ball Neural ODEs
Proper orthogonal decomposition (POD) allows reduced-order modeling of complex dynamical systems at a substantial level, while maintaining a high...
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Structuring Data with Block Term Decomposition: Decomposition of Joint Tensors and Variational Block Term Decomposition as a Parametrized Mixture Distribution Model
AbstractThe idea of using tensor decompositions as a parametric model for group data analysis is developed. Two models (deterministic and...
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Robust Two-Stage Estimation in General Spatial Dynamic Panel Data Models
This paper proposes a robust two-stage estimation procedure for a general spatial dynamic panel data model in light of the two-stage estimation...
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Wavelet adaptive proper orthogonal decomposition for large-scale flow data
The proper orthogonal decomposition (POD) is a powerful classical tool in fluid mechanics used, for instance, for model reduction and extraction of...
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Sliding mode fault-tolerant control for T–S fuzzy system: a singular system approach
The problem of sliding mode fault-tolerant control (SMFTC) for T–S fuzzy systems is addressed in this paper. The case that the fuzzy system has...
