Feedthrough is the mechanism whereby a perturbation at one interface affects the evolution of perturbations at nearby interfaces. In this paper, we identify detrimental as well as beneficial effects of feedthrough in Rayleigh–Taylor (RT) and Richtmyer–Meshkov (RM) instabilities, and define and evaluate a feedthrough effectiveness factor FRT and FRM for each instability, finding that FRM > FRT. We determine what initial conditions are required to suppress RT and RM instabilities. Asymptotic decay calls for a carefully tuned shock at an interface just as it begins to accelerate in the opposite direction. We examine freeze out and mode killing, which rely on feedthrough and require nonzero perturbations in a specific ratio at the two interfaces of a finite-thickness shell. We also present model-based predictions on feedthrough in turbulent RT and RM mixing layers. These stabilizing mechanisms may be useful in hot spot as well as shock-ignition inertial confinement fusion designs explaining, perhaps, the unpredictability and variability in the performance of nominally identical capsules, and how a poorer quality capsule can outperform a higher-quality one, as apparently happened in the recently reported National Ignition Facility experiment N221204 [Abu-Shawareb et al., Phys. Rev. Lett. 132, 065102 (2024).]
Clark et al. [Phys. Plasmas 31, 062706 (2024)] present calculations on modeling ablator defects in high-performance implosions on the National Ignition Facility (NIF). They apply their methodology to an early NIF shot N210808 and a later, more famous shot N221204, which broke the barrier for target gain greater than one [H. Abu-Shawareb et al., Phys. Rev. Lett. 132, 065102 (2024)], claiming a “plausible explanation” for their performances. We make three comments disputing that conclusion.
Recent studies have highlighted the predictive value of combined Immunoglobulin Free Light Chain (FLC) levels in circulatory disease (CD), as well as other life-threatening conditions. Elevated FLC levels have been associated with poor outcomes, including increased mortality and hospital readmissions. In acute Heart Failure (HF), for example, patients had significantly higher FLC levels compared to those with stable HF or healthy controls. Moreover, FLC levels remained elevated long after clinical stabilization, suggesting potential ongoing immune disturbances, or impaired renal clearance. The present study investigated the use of Machine Learning (ML) techniques to predict the survival of patients with elevated FLC and to improve the understanding of circulatory mortality risks by identifying individuals whose deaths were attributable to circulatory system conditions. It was found that binary classification models could successfully determine patients with negative outcome (AUC > 0.80), and survival analysis models could achieve dynamic AUC higher than 0.75 in estimating the survival probability as a function of time. In the context of CD, elevated levels of FLCs have been shown to be associated with an increased risk of circulatory events. This association suggests that FLCs might play a role in the development of circulatory conditions.
Data-driven constitutive modeling frameworks based on neural networks and classical representation theorems have recently gained considerable attention due to their ability to easily incorporate constitutive constraints and their excellent generalization performance. In these models, the stress prediction follows from a linear combination of invariant-dependent coefficient functions and known tensor basis generators. However, thus far the formulations have been limited to stress representations based on the classical Rivlin and Ericksen form, while the performance of alternative representations has yet to be investigated. In this work, we survey a variety of tensor basis neural network models for modeling hyperelastic materials in a finite deformation context, including a number of so far unexplored formulations which use theoretically equivalent invariants and generators to Finger-Rivlin-Ericksen. Furthermore, we compare potential-based and coefficient-based approaches, as well as different calibration techniques. Nine variants are tested against both noisy and noiseless datasets for three different materials. Theoretical and practical insights into the performance of each formulation are given.
