Pages that link to "Item:Q3183347"
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The following pages link to Diffusion Maps, Reduction Coordinates, and Low Dimensional Representation of Stochastic Systems (Q3183347):
Displaying 50 items.
- Local kernels and the geometric structure of data (Q265690) (← links)
- Diffusion-based kernel methods on Euclidean metric measure spaces (Q285541) (← links)
- Deciphering interactions of complex systems that do not satisfy detailed balance (Q482933) (← links)
- Tensor networks and hierarchical tensors for the solution of high-dimensional partial differential equations (Q506609) (← links)
- A spectral notion of Gromov-Wasserstein distance and related methods (Q533501) (← links)
- Data-driven model reduction and transfer operator approximation (Q722011) (← links)
- Variable bandwidth diffusion kernels (Q900774) (← links)
- Multiscale analysis of collective motion and decision-making in swarms: an advection-diffusion equation with memory approach (Q1719770) (← links)
- Transition manifolds of complex metastable systems. Theory and data-driven computation of effective dynamics (Q1744114) (← links)
- Diffusion maps-aided neural networks for the solution of parametrized PDEs (Q2021984) (← links)
- Dimensionality reduction of complex metastable systems via kernel embeddings of transition manifolds (Q2022651) (← links)
- Spectral thresholding for the estimation of Markov chain transition operators (Q2074325) (← links)
- Committor functions via tensor networks (Q2099715) (← links)
- Data-driven efficient solvers for Langevin dynamics on manifold in high dimensions (Q2105115) (← links)
- Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems (Q2125437) (← links)
- Supersymmetric quantum mechanics method for the Fokker-Planck equation with applications to protein folding dynamics (Q2148380) (← links)
- Inverting nonlinear dimensionality reduction with scale-free radial basis function interpolation (Q2252510) (← links)
- Diffusion maps tailored to arbitrary non-degenerate Itô processes (Q2278457) (← links)
- Solving for high-dimensional committor functions using artificial neural networks (Q2319851) (← links)
- Inequalities for overdamped fluctuating systems (Q2322241) (← links)
- Landmark diffusion maps (L-dMaps): accelerated manifold learning out-of-sample extension (Q2424630) (← links)
- Diffusion maps, spectral clustering and reaction coordinates of dynamical systems (Q2497982) (← links)
- Data-driven control of agent-based models: an equation/variable-free machine learning approach (Q2687520) (← links)
- Computing committors in collective variables via Mahalanobis diffusion maps (Q2689136) (← links)
- Staying the course: iteratively locating equilibria of dynamical systems on Riemannian manifolds defined by point-clouds (Q2696361) (← links)
- Nonparametric Uncertainty Quantification for Stochastic Gradient Flows (Q2945163) (← links)
- Convergence of Equation-Free Methods in the Case of Finite Time Scale Separation with Application to Deterministic and Stochastic Systems (Q4562419) (← links)
- Reduction for Stochastic Biochemical Reaction Networks with Multiscale Conservations (Q4601597) (← links)
- Spectral Methods for Multiscale Stochastic Differential Equations (Q4636405) (← links)
- Point Cloud Discretization of Fokker--Planck Operators for Committor Functions (Q4643814) (← links)
- Data-adaptive harmonic spectra and multilayer Stuart-Landau models (Q4644288) (← links)
- Diffusion State Distances: Multitemporal Analysis, Fast Algorithms, and Applications to Biological Networks (Q4999351) (← links)
- Modern Koopman Theory for Dynamical Systems (Q5075835) (← links)
- Learning Markov Models Via Low-Rank Optimization (Q5106374) (← links)
- Multilevel approximation of Gaussian random fields: Fast simulation (Q5112023) (← links)
- Geometric fluid approximation for general continuous-time Markov chains (Q5160758) (← links)
- Local and global perspectives on diffusion maps in the analysis of molecular systems (Q5160841) (← links)
- (Q5214254) (← links)
- Kernel Methods for the Approximation of Nonlinear Systems (Q5348477) (← links)
- Numerical Model Construction with Closed Observables (Q5506822) (← links)
- ATLAS: A Geometric Approach to Learning High-Dimensional Stochastic Systems Near Manifolds (Q5737746) (← links)
- Continuation with Noninvasive Control Schemes: Revealing Unstable States in a Pedestrian Evacuation Scenario (Q5887840) (← links)
- Random Walk Approximation for Irreversible Drift-Diffusion Process on Manifold: Ergodicity, Unconditional Stability and Convergence (Q6049605) (← links)
- Data-driven probability density forecast for stochastic dynamical systems (Q6054200) (← links)
- Grassmannian diffusion maps based surrogate modeling via geometric harmonics (Q6070089) (← links)
- Data-Driven Discovery of Governing Equations for Coarse-Grained Heterogeneous Network Dynamics (Q6076413) (← links)
- Physics-agnostic and physics-infused machine learning for thin films flows: modelling, and predictions from small data (Q6086911) (← links)
- Bottom-Up Transient Time Models in Coarse-Graining Molecular Systems (Q6088334) (← links)
- CSPlib: a performance portable parallel software toolkit for analyzing complex kinetic mechanisms (Q6124641) (← links)
- Nonlinear model reduction for slow-fast stochastic systems near unknown invariant manifolds (Q6188980) (← links)
