Pages that link to "Item:Q2133556"

The following pages link to Physics-informed machine learning for reduced-order modeling of nonlinear problems (Q2133556):

Displaying 36 items.

• A hybrid partitioned deep learning methodology for moving interface and fluid-structure interaction (Q2072360) (← links)
• A non-intrusive model order reduction approach for parameterized time-domain Maxwell's equations (Q2083308) (← links)
• Bridging the gap: machine learning to resolve improperly modeled dynamics (Q2116291) (← links)
• Discovery of subdiffusion problem with noisy data via deep learning (Q2149161) (← links)
• Model order reduction method based on (r)POD-ANNs for parameterized time-dependent partial differential equations (Q2158140) (← links)
• A non-intrusive neural network model order reduction algorithm for parameterized parabolic PDEs (Q2159858) (← links)
• Retracted: Model order reduction method based on machine learning for parameterized time-dependent partial differential equations (Q2161825) (← links)
• The deep parametric PDE method and applications to option pricing (Q2161843) (← links)
• Neural-network learning of SPOD latent dynamics (Q2168295) (← links)
• Machine learning augmented reduced-order models for FFR-prediction (Q2237421) (← links)
• DeepParticle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method (Q2672762) (← links)
• Physics-data combined machine learning for parametric reduced-order modelling of nonlinear dynamical systems in small-data regimes (Q2678495) (← links)
• A deep learning approach to Reduced Order Modelling of parameter dependent partial differential equations (Q5058646) (← links)
• CoolPINNs: a physics-informed neural network modeling of active cooling in vascular systems (Q6072827) (← links)
• Mesh-informed neural networks for operator learning in finite element spaces (Q6077303) (← links)
• Surrogate modeling of time-domain electromagnetic wave propagation via dynamic mode decomposition and radial basis function (Q6095088) (← links)
• A New Certified Hierarchical and Adaptive RB-ML-ROM Surrogate Model for Parametrized PDEs (Q6097873) (← links)
• Data-driven reduced order modelling for patient-specific hemodynamics of coronary artery bypass grafts with physical and geometrical parameters (Q6101879) (← links)
• A graph convolutional autoencoder approach to model order reduction for parametrized PDEs (Q6126547) (← links)
• Model order reduction for parameterized electromagnetic problems using matrix decomposition and deep neural networks (Q6137793) (← links)
• Multi-fidelity physics constrained neural networks for dynamical systems (Q6153908) (← links)
• Model reduction of coupled systems based on non-intrusive approximations of the boundary response maps (Q6153912) (← links)
• An artificial neural network approach to bifurcating phenomena in computational fluid dynamics (Q6158472) (← links)
• Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation (Q6159004) (← links)
• Active-learning-driven surrogate modeling for efficient simulation of parametric nonlinear systems (Q6185211) (← links)
• Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations (Q6187659) (← links)
• Parametric nonlinear model reduction using machine learning on Grassmann manifold with an application on a flow simulation (Q6536761) (← links)
• Reduction of the shallow water system by an error aware POD-neural network method: application to floodplain dynamics (Q6566077) (← links)
• Slow invariant manifolds of singularly perturbed systems via physics-informed machine learning (Q6573172) (← links)
• Non-intrusive reduced-order model for time-dependent stochastic partial differential equations utilizing dynamic mode decomposition and polynomial chaos expansion (Q6592584) (← links)
• Mathematical modelling and computational reduction of molten glass fluid flow in a furnace melting basin (Q6616176) (← links)
• Domain decomposition for physics-data combined neural network based parametric reduced order modelling (Q6639365) (← links)
• PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs (Q6643563) (← links)
• GFN: a graph feedforward network for resolution-invariant reduced operator learning in multifidelity applications (Q6643617) (← links)
• Slow invariant manifolds of fast-slow systems of ODEs with physics-informed neural networks (Q6661630) (← links)
• A multi-field decomposed model order reduction approach for thermo-mechanically coupled gradient-extended damage simulations (Q6669045) (← links)