Pages that link to "Item:Q2184334"
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The following pages link to Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems (Q2184334):
Displaying 26 items.
- Interface PINNs (I-PINNs): a physics-informed neural networks framework for interface problems (Q6588288) (← links)
- Partitioned neural network approximation for partial differential equations enhanced with Lagrange multipliers and localized loss functions (Q6588333) (← links)
- Iterative algorithms for partitioned neural network approximation to partial differential equations (Q6590244) (← links)
- Advanced physics-informed neural networks for numerical approximation of the coupled Schrödinger-KdV equation (Q6590978) (← links)
- Energy-informed graph transformer model for solid mechanical analyses (Q6591778) (← links)
- Data-driven rogue waves solutions for the focusing and variable coefficient nonlinear Schrödinger equations via deep learning (Q6592624) (← links)
- Physics-informed deep learning of rate-and-state fault friction (Q6595877) (← links)
- Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning (Q6598418) (← links)
- Deep learning in computational mechanics: a review (Q6604128) (← links)
- Multistep asymptotic pre-training strategy based on PINNs for solving steep boundary singular perturbation problems (Q6609750) (← links)
- Interpretable physics-encoded finite element network to handle concentration features and multi-material heterogeneity in hyperelasticity (Q6609781) (← links)
- Binary structured physics-informed neural networks for solving equations with rapidly changing solutions (Q6615737) (← links)
- Domain decomposition algorithms for neural network approximation of partial differential equations (Q6620256) (← links)
- Enhancing training of physics-informed neural networks using domain decomposition-based preconditioning strategies (Q6623675) (← links)
- Higher-order multi-scale physics-informed neural network (HOMS-PINN) method and its convergence analysis for solving elastic problems of authentic composite materials (Q6633295) (← links)
- Domain decomposition for physics-data combined neural network based parametric reduced order modelling (Q6639365) (← links)
- Deep adaptive sampling for surrogate modeling without labeled data (Q6639518) (← links)
- MHDnet: physics-preserving learning for solving magnetohydrodynamics problems (Q6646462) (← links)
- Prediction of spatiotemporal dynamics using deep learning: coupled neural networks of long short-terms memory, auto-encoder and physics-informed neural networks (Q6650113) (← links)
- Simple yet effective adaptive activation functions for physics-informed neural networks (Q6660250) (← links)
- A stabilized physics informed neural networks method for wave equations (Q6662428) (← links)
- NeuroSEM: a hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements (Q6663315) (← links)
- A novel data-driven framework of elastoplastic constitutive model based on geometric physical information (Q6663333) (← links)
- Kolmogorov-Arnold-informed neural network: a physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov-Arnold networks (Q6669014) (← links)
- Taylor series error correction network for super-resolution of discretized partial differential equation solutions (Q6669099) (← links)
- Adaptive deep density approximation for stochastic dynamical systems (Q6671865) (← links)
