A PINN-based periodic CFD solver is shown to reach nearly the same accuracy as traditional transient-to-periodic methods but with substantially lower computational time for 2D heat diffusion and fluid flow cases.
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics- informed neural networks
4 Pith papers cite this work. Polarity classification is still indexing.
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Bio-PINNs with a near-to-far curriculum and deformation-uncertainty proxy recover cell-induced densified phases and tether morphologies more reliably than standard adaptive PINN baselines in single-cell and multicellular settings.
An auto-adaptive sampling technique for PINNs is introduced and tested on Allen-Cahn equations to better resolve interfacial regions compared to residual-adaptive methods.
A reinforcement learning policy learns to adaptively harvest data samples, improving empirical constraint satisfaction and training efficiency for Lyapunov NNs and PINNs.
citing papers explorer
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Physics Informed Neural Network-based Computational Method for Accelerating Time-Periodic Unsteady CFD Simulations
A PINN-based periodic CFD solver is shown to reach nearly the same accuracy as traditional transient-to-periodic methods but with substantially lower computational time for 2D heat diffusion and fluid flow cases.
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Cell-induced densification and tether formation in fibrous extracellular matrices with biomimetic physics-informed neural networks
Bio-PINNs with a near-to-far curriculum and deformation-uncertainty proxy recover cell-induced densified phases and tether morphologies more reliably than standard adaptive PINN baselines in single-cell and multicellular settings.
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Auto-Adaptive PINNs with Applications to Phase Transitions
An auto-adaptive sampling technique for PINNs is introduced and tested on Allen-Cahn equations to better resolve interfacial regions compared to residual-adaptive methods.
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Adaptive Data Harvesting for Efficient Neural Network Learning with Universal Constraints
A reinforcement learning policy learns to adaptively harvest data samples, improving empirical constraint satisfaction and training efficiency for Lyapunov NNs and PINNs.