By recasting continuous-forcing immersed boundary problems as composite interior-exterior fields with a smoothed indicator function, the method incorporates neglected terms to achieve second-order convergence in Poisson problems and near-second-order in incompressible Navier-Stokes flows, while also
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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 sharp-interface VOF method for phase-change simulations on unstructured meshes computes evaporation rates from local temperature gradients at geometrically reconstructed interfaces and validates against analytical solutions on Stefan, Sucking, and Scriven problems.
Bayesian PINNs with Hamiltonian Monte Carlo sampling deliver the most consistent uncertainty estimates for turbulent flow inverse problems, while repulsive deep ensembles provide a faster but slightly less calibrated alternative.
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Beyond first-order accuracy in continuous-forcing immersed boundary methods, and their well-conditioned projection-based solution
By recasting continuous-forcing immersed boundary problems as composite interior-exterior fields with a smoothed indicator function, the method incorporates neglected terms to achieve second-order convergence in Poisson problems and near-second-order in incompressible Navier-Stokes flows, while also
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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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Sharp-interface VOF method for phase-change simulations on unstructured meshes
A sharp-interface VOF method for phase-change simulations on unstructured meshes computes evaporation rates from local temperature gradients at geometrically reconstructed interfaces and validates against analytical solutions on Stefan, Sucking, and Scriven problems.
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Uncertainty Quantification in PINNs for Turbulent Flows: Bayesian Inference and Repulsive Ensembles
Bayesian PINNs with Hamiltonian Monte Carlo sampling deliver the most consistent uncertainty estimates for turbulent flow inverse problems, while repulsive deep ensembles provide a faster but slightly less calibrated alternative.