PINN study of BGK shocks identifies anisotropic tail-weighted observability failure in fourth-order closure R_xx^cl and shows a shock-local correction reduces its relative error to 0.112 using DVM validation.
Asymptotic-preserving neural networks for multiscale kinetic equations
3 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
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PhysicsFormer applies a lightweight Transformer PINN with pseudo-sequential representations to convection, Burgers, lid-driven cavity, and inverse Navier-Stokes problems, reporting near-zero error in parameter identification and flow reconstruction from sparse noisy data.
An expository review of common structures in asymptotic analysis across scales and their potential as inputs for AI-assisted formalization tools.
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Tail observability and fourth-order closure recovery in physics-informed neural networks for Bhatnagar-Gross-Krook normal shocks
PINN study of BGK shocks identifies anisotropic tail-weighted observability failure in fourth-order closure R_xx^cl and shows a shock-local correction reduces its relative error to 0.112 using DVM validation.
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A Simple but Efficient Transformer-Based Physics-Informed Neural Network for Incompressible Navier--Stokes Equations
PhysicsFormer applies a lightweight Transformer PINN with pseudo-sequential representations to convection, Burgers, lid-driven cavity, and inverse Navier-Stokes problems, reporting near-zero error in parameter identification and flow reconstruction from sparse noisy data.
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Bridging Scales: Asymptotic Analysis and AI-Assisted Formalization
An expository review of common structures in asymptotic analysis across scales and their potential as inputs for AI-assisted formalization tools.