Pages that link to "Item:Q5165440"
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The following pages link to Physics-Informed Neural Networks with Hard Constraints for Inverse Design (Q5165440):
Displaying 20 items.
- Recovering the source term in elliptic equation via deep learning: method and convergence analysis (Q6586293) (← links)
- Variational temporal convolutional networks for I-FENN thermoelasticity (Q6588274) (← links)
- Solving large-scale variational inequalities with dynamically adjusting initial condition in physics-informed neural networks (Q6588313) (← links)
- A meshless solver for blood flow simulations in elastic vessels using a physics-informed neural network (Q6590130) (← links)
- Phase field smoothing-PINN: a neural network solver for partial differential equations with discontinuous coefficients (Q6590262) (← links)
- Exact enforcement of temporal continuity in sequential physics-informed neural networks (Q6595862) (← links)
- Physics-specialized neural network with hard constraints for solving multi-material diffusion problems (Q6595894) (← links)
- Deep learning in computational mechanics: a review (Q6604128) (← links)
- Solving high-dimensional parametric engineering problems for inviscid flow around airfoils based on physics-informed neural networks (Q6615001) (← 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)
- A new method to compute the blood flow equations using the physics-informed neural operator (Q6639295) (← links)
- KAN-ODEs: Kolmogorov-Arnold network ordinary differential equations for learning dynamical systems and hidden physics (Q6641934) (← links)
- Tackling the curse of dimensionality in fractional and tempered fractional PDEs with physics-informed neural networks (Q6643609) (← links)
- A multifidelity machine learning based semi-Lagrangian finite volume scheme for linear transport equations and the nonlinear Vlasov-Poisson system (Q6644357) (← links)
- An analysis and solution of ill-conditioning in physics-informed neural networks (Q6648404) (← links)
- The ADMM-PINNs algorithmic framework for nonsmooth PDE-constrained optimization: a deep learning approach (Q6649881) (← links)
- Bayesian inverse Navier-Stokes problems: joint flow field reconstruction and parameter learning (Q6659678) (← links)
- I-FENN for thermoelasticity based on physics-informed temporal convolutional network (PI-TCN) (Q6661937) (← links)
- NeuroSEM: a hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements (Q6663315) (← links)
- Navigating PINNs via maximum residual-based continuous distribution (Q6669778) (← links)
