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.
- An alternating flux learning method for multidimensional nonlinear conservation laws (Q6585306) (← links)
- Mixing artificial and natural intelligence: from statistical mechanics to AI and back to turbulence (Q6585765) (← links)
- Optimal reconstruction of vector fields from data for prediction and uncertainty quantification (Q6588228) (← links)
- Koopman dynamic-oriented deep learning for invariant subspace identification and full-state prediction of complex systems (Q6588249) (← links)
- tLaSDI: thermodynamics-informed latent space dynamics identification (Q6588297) (← links)
- Data-driven identification of stable sparse differential operators using constrained regression (Q6588307) (← links)
- Physics-constrained symbolic model discovery for polyconvex incompressible hyperelastic materials (Q6589318) (← links)
- Ml-GLE: a machine learning enhanced generalized Langevin equation framework for transient anomalous diffusion in polymer dynamics (Q6589873) (← links)
- Physics-informed genetic programming for discovery of partial differential equations from scarce and noisy data (Q6589933) (← links)
- Gaussian process learning of nonlinear dynamics (Q6590926) (← links)
- On higher order drift and diffusion estimates for stochastic SINDy (Q6592244) (← links)
- Pattern formation in dense populations studied by inference of nonlinear diffusion-reaction mechanisms (Q6592335) (← links)
- On sparse regression, \(L_p\)-regularization, and automated model discovery (Q6592362) (← links)
- Tipping points of evolving epidemiological networks: machine learning-assisted, data-driven effective modeling (Q6592553) (← links)
- Enhancing model identification with SINDy via nullcline reconstruction (Q6592562) (← links)
- Data-driven modeling of equatorial atmospheric waves: the role of moisture and nonlinearity on global-scale instabilities and propagation speeds (Q6592566) (← links)
- An optimization approach to establish dynamical equivalence for soft and rigid impact models (Q6592590) (← links)
- Identification method for a fractional-order system in terms of equivalent dynamic properties (Q6592603) (← links)
- Model adaptive phase space reconstruction (Q6592610) (← links)
- Stepwise reconstruction of higher-order networks from dynamics (Q6592656) (← links)
- CGNSDE: conditional Gaussian neural stochastic differential equation for modeling complex systems and data assimilation (Q6592766) (← links)
- Machine learning approaches for the solution of the Riemann problem in fluid dynamics: a case study (Q6593781) (← links)
- Physics-based active learning for design space exploration and surrogate construction for multiparametric optimization (Q6593783) (← links)
- Estimating time-varying reproduction number by deep learning techniques (Q6597354) (← links)
- Error analysis based on inverse modified differential equations for discovery of dynamics using linear multistep methods and deep learning (Q6601198) (← links)
- Differential equations in data analysis (Q6602133) (← links)
- Deep learning in computational mechanics: a review (Q6604128) (← links)
- Dynamical system identification, model selection, and model uncertainty quantification by Bayesian inference (Q6604850) (← links)
- Model reduction of dynamical systems with a novel data-driven approach: the RC-HAVOK algorithm (Q6604859) (← links)
- Identifiability implies robust, globally exponentially convergent on-line parameter estimation (Q6605843) (← links)
- An identification method for oscillators with response-dependent inertia (Q6605968) (← links)
- Online identification and control of PDEs via reinforcement learning methods (Q6607032) (← links)
- EKF-SINDy: empowering the extended Kalman filter with sparse identification of nonlinear dynamics (Q6609776) (← links)
- Data-driven transient lift attenuation for extreme vortex gust-airfoil interactions (Q6609855) (← links)
- Data-driven delay identification with SINDy (Q6610956) (← links)
- Physics-enhanced sparse identification of nonlinear oscillator with Coulomb friction (Q6610960) (← links)
- \(\Phi\)-DVAE: physics-informed dynamical variational autoencoders for unstructured data assimilation (Q6614997) (← links)
- Machine learning in viscoelastic fluids via energy-based kernel embedding (Q6615024) (← links)
- Adaptive finite horizon degradation-aware regulator (Q6618934) (← links)
- Degradation simulator for infinite horizon controlled linear time-invariant systems (Q6618937) (← links)
- Dimensional reduction of dynamical systems by machine learning: automatic generation of the optimum extensive variables and their time-evolution map (Q6620210) (← links)
- On the identifiability of nonlocal interaction kernels in first-order systems of interacting particles on Riemannian manifolds (Q6620727) (← links)
- Learning fluid physics from highly turbulent data using sparse physics-informed discovery of empirical relations (SPIDER) (Q6621776) (← links)
- Optimal control of open-loop multibody systems recovered from data (Q6622368) (← links)
- Machine learning methods for reduced order modeling (Q6629175) (← links)
- Implementation and (inverse modified) error analysis for implicitly templated ODE-nets (Q6629683) (← links)
- A stochastic gradient-based two-step sparse identification algorithm for multivariate ARX systems (Q6631019) (← links)
- Gaussian Process Assisted Active Learning of Physical Laws (Q6631893) (← links)
- Forecasting and predicting stochastic agent-based model data with biologically-informed neural networks (Q6632673) (← links)
- A discretization-invariant extension and analysis of some deep operator networks (Q6633297) (← links)
