INEUS solves high-dimensional PIDEs via iterative neural regression with single-jump sampling instead of full integral evaluation.
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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.
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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.