INEUS solves high-dimensional PIDEs via iterative neural regression with single-jump sampling instead of full integral evaluation.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 3years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
A velocity-weighted L2 loss for PINNs on the BGK model guarantees convergence to the physical solution by penalizing high-velocity errors.
Asymptotic-preserving neural networks infer viscoelastic parameters and reconstruct blood vessel state evolution from accessible ultrasound data in multiscale arterial flow models.
citing papers explorer
-
INEUS: Iterative Neural Solver for High-Dimensional PIDEs
INEUS solves high-dimensional PIDEs via iterative neural regression with single-jump sampling instead of full integral evaluation.
-
A Theory-guided Weighted $L^2$ Loss for solving the BGK model via Physics-informed neural networks
A velocity-weighted L2 loss for PINNs on the BGK model guarantees convergence to the physical solution by penalizing high-velocity errors.
-
Asymptotic-Preserving Neural Networks for Viscoelastic Parameter Identification in Multiscale Blood Flow Modeling
Asymptotic-preserving neural networks infer viscoelastic parameters and reconstruct blood vessel state evolution from accessible ultrasound data in multiscale arterial flow models.