Pages that link to "Item:Q5383200"

The following pages link to Linearly Recurrent Autoencoder Networks for Learning Dynamics (Q5383200):

Displaying 50 items.

• Modelling the dynamics of nonlinear partial differential equations using neural networks (Q596178) (← links)
• Deep learning for model order reduction of multibody systems to minimal coordinates (Q2020838) (← links)
• Inverse multiobjective optimization: inferring decision criteria from data (Q2038913) (← links)
• Data-driven operator theoretic methods for phase space learning and analysis (Q2083237) (← links)
• Lift \& learn: physics-informed machine learning for large-scale nonlinear dynamical systems (Q2115511) (← links)
• A long short-term memory embedding for hybrid uplifted reduced order models (Q2125587) (← links)
• A data-driven, physics-informed framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings (Q2129328) (← links)
• State estimation with limited sensors -- a deep learning based approach (Q2135833) (← links)
• tgEDMD: approximation of the Kolmogorov operator in tensor train format (Q2146443) (← links)
• Assessment of end-to-end and sequential data-driven learning for non-intrusive modeling of fluid flows (Q2190672) (← links)
• Dynamics reconstruction and classification via Koopman features (Q2218387) (← links)
• Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning (Q2222332) (← links)
• Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders (Q2223001) (← links)
• Variational approach for learning Markov processes from time series data (Q2303757) (← links)
• Learning nonlinear state-space models using autoencoders (Q2665158) (← links)
• Parametric dynamic mode decomposition for reduced order modeling (Q2683069) (← links)
• Model reduction for the material point method via an implicit neural representation of the deformation map (Q2687512) (← links)
• Data-Driven Model Predictive Control using Interpolated Koopman Generators (Q4983502) (← links)
• Sparsity-promoting algorithms for the discovery of informative Koopman-invariant subspaces (Q4987934) (← links)
• On Koopman mode decomposition and tensor component analysis (Q4993697) (← links)
• (Q4998931) (← links)
• A Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data Using Unstructured Spatial Discretizations (Q5005016) (← links)
• Deep learning models for global coordinate transformations that linearise PDEs (Q5014841) (← links)
• Koopman analysis of quantum systems* (Q5057844) (← links)
• Modern Koopman Theory for Dynamical Systems (Q5075835) (← links)
• Generative Stochastic Modeling of Strongly Nonlinear Flows with Non-Gaussian Statistics (Q5097837) (← links)
• Time-Delay Observables for Koopman: Theory and Applications (Q5109371) (← links)
• Physics-Informed Probabilistic Learning of Linear Embeddings of Nonlinear Dynamics with Guaranteed Stability (Q5109771) (← links)
• Leveraging reduced-order models for state estimation using deep learning (Q5113091) (← links)
• The structure of reconstructed flows in latent spaces (Q5139746) (← links)
• Kernel methods for detecting coherent structures in dynamical data (Q5213519) (← links)
• Scalable Extended Dynamic Mode Decomposition Using Random Kernel Approximation (Q5230606) (← links)
• Data-Driven Identification of Parametric Partial Differential Equations (Q5383204) (← links)
• Adaptive path-integral autoencoder: representation learning and planning for dynamical systems (Q5854109) (← links)
• Nonintrusive Reduced Order Modelling of Convective Boussinesq Flows (Q5880413) (← links)
• Koopman analysis of nonlinear systems with a neural network representation (Q6043737) (← links)
• Transformers for modeling physical systems (Q6055222) (← links)
• Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution (Q6068233) (← links)
• Deep Koopman model predictive control for enhancing transient stability in power grids (Q6089850) (← links)
• A study on data-driven identification and representation of nonlinear dynamical systems with a physics-integrated deep learning approach: Koopman operators and nonlinear normal modes (Q6105292) (← links)
• Generalizing dynamic mode decomposition: balancing accuracy and expressiveness in Koopman approximations (Q6110261) (← links)
• Parsimony as the ultimate regularizer for physics-informed machine learning (Q6117148) (← links)
• Koopman operator learning using invertible neural networks (Q6126575) (← links)
• Neural dynamic mode decomposition for end-to-end modeling of nonlinear dynamics (Q6166776) (← links)
• Rigorous data‐driven computation of spectral properties of Koopman operators for dynamical systems (Q6180710) (← links)
• Active-learning-driven surrogate modeling for efficient simulation of parametric nonlinear systems (Q6185211) (← links)
• Enhancing predictive capabilities in data-driven dynamical modeling with automatic differentiation: Koopman and neural ODE approaches (Q6554429) (← links)
• Propagating uncertainty through system dynamics in reproducing kernel Hilbert space (Q6554937) (← links)
• Data-driven identification of dynamical models using adaptive parameter sets (Q6561192) (← links)
• Koopman neural operator as a mesh-free solver of non-linear partial differential equations (Q6572200) (← links)