Pages that link to "Item:Q137310"
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The following pages link to Discovering governing equations from data by sparse identification of nonlinear dynamical systems (Q137310):
Displaying 50 items.
- Discovering phase field models from image data with the pseudo-spectral physics informed neural networks (Q2667357) (← links)
- Hamiltonian operator inference: physics-preserving learning of reduced-order models for canonical Hamiltonian systems (Q2670214) (← links)
- On data-driven stabilization of systems with nonlinearities satisfying quadratic constraints (Q2670734) (← links)
- Low-rank statistical finite elements for scalable model-data synthesis (Q2671375) (← links)
- Operator inference and physics-informed learning of low-dimensional models for incompressible flows (Q2672189) (← links)
- ModalPINN: an extension of physics-informed neural networks with enforced truncated Fourier decomposition for periodic flow reconstruction using a limited number of imperfect sensors (Q2672754) (← links)
- Scalable uncertainty quantification for deep operator networks using randomized priors (Q2674111) (← links)
- LaSDI: parametric latent space dynamics identification (Q2674132) (← links)
- Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference (Q2678552) (← links)
- Thermodynamically consistent machine-learned internal state variable approach for data-driven modeling of path-dependent materials (Q2679297) (← links)
- Automated learning of interpretable models with quantified uncertainty (Q2679495) (← links)
- Sparse dynamical system identification with simultaneous structural parameters and initial condition estimation (Q2680004) (← links)
- Machine learning moment closure models for the radiative transfer equation. III: enforcing hyperbolicity and physical characteristic speeds (Q2680325) (← links)
- On the universal transformation of data-driven models to control systems (Q2681379) (← links)
- Physics-informed neural networks for data-driven simulation: advantages, limitations, and opportunities (Q2683126) (← links)
- Automated discovery of generalized standard material models with EUCLID (Q2683452) (← links)
- Data-driven learning of differential equations: combining data and model uncertainty (Q2686515) (← links)
- Data-driven control of agent-based models: an equation/variable-free machine learning approach (Q2687520) (← links)
- Deep physics corrector: a physics enhanced deep learning architecture for solving stochastic differential equations (Q2687567) (← links)
- Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning (Q2688065) (← links)
- Reconstruction of delay differential equations via learning parameterized dictionary (Q2688070) (← links)
- Data-driven forward and inverse problems for chaotic and hyperchaotic dynamic systems based on two machine learning architectures (Q2688074) (← links)
- Local parameter identification with neural ordinary differential equations (Q2690025) (← links)
- Mini-workshop: Analysis of data-driven optimal control. Abstracts from the mini-workshop held May 9--15, 2021 (hybrid meeting) (Q2693004) (← links)
- Learning elliptic partial differential equations with randomized linear algebra (Q2697403) (← links)
- WeakIdent: weak formulation for identifying differential equation using narrow-fit and trimming (Q2699369) (← links)
- Forecasting of nonlinear dynamics based on symbolic invariance (Q2701225) (← links)
- Manifold learning for organizing unstructured sets of process observations (Q3303828) (← links)
- Reinforcement-learning-based control of confined cylinder wakes with stability analyses (Q3383631) (← links)
- Stochastic modelling of a noise-driven global instability in a turbulent swirling jet (Q3389421) (← links)
- Automated reverse engineering of nonlinear dynamical systems (Q3615247) (← links)
- Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks (Q4557699) (← links)
- Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations (Q4558167) (← links)
- Extracting Sparse High-Dimensional Dynamics from Limited Data (Q4561658) (← links)
- Data-Driven Discovery of Closure Models (Q4562411) (← links)
- Sparse reduced-order modelling: sensor-based dynamics to full-state estimation (Q4563901) (← links)
- Data-Driven Filtered Reduced Order Modeling of Fluid Flows (Q4568096) (← links)
- Generalizing Koopman Theory to Allow for Inputs and Control (Q4571161) (← links)
- Nonlinear waves in a simple model of high-grade glioma (Q4597664) (← links)
- Online Interpolation Point Refinement for Reduced-Order Models using a Genetic Algorithm (Q4603498) (← links)
- Robust data-driven discovery of governing physical laws with error bars (Q4626135) (← links)
- Sparse Recovery and Dictionary Learning to Identify the Nonlinear Dynamical Systems: One Step Toward Finding Bifurcation Points in Real Systems (Q4631662) (← links)
- Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator (Q4644309) (← links)
- Model selection for dynamical systems via sparse regression and information criteria (Q4644829) (← links)
- Learning partial differential equations via data discovery and sparse optimization (Q4646838) (← links)
- System identification of a low-density jet via its noise-induced dynamics (Q4647430) (← links)
- NONPARAMETRIC MODELING AND SPATIOTEMPORAL DYNAMICAL SYSTEMS (Q4655672) (← links)
- On Matching, and Even Rectifying, Dynamical Systems through Koopman Operator Eigenfunctions (Q4686615) (← links)
- The turbulent dynamo (Q4964123) (← links)
- Robust and optimal sparse regression for nonlinear PDE models (Q4972990) (← links)
