Volume 1 Issue 11, November 2021

Compressing scientific data is essential to save on storage space, but doing so effectively while ensuring that the conclusions from the data are not affected remains a challenging task. A recent paper proposes a new method to identify numerical noise from floating-point atmospheric data, which can lead to a more effective compression.

Climate data are often stored at higher precision than is needed. The proposed compression automatically determines the precision from the data’s bitwise real information, removing any false information and leading to a more efficient compression.

The authors propose a molecular modeling approach to simulating the galvanostatic charge–discharge process of supercapacitors under constant-potential conditions. This model can accurately predict supercapacitor dynamics when compared with experimental observations.

Combining bioinformatics data and atomistic simulations, this study develops a sequence-dependent coarse-grained model for biomolecular phase separation. This model achieves a quantitative agreement with experimental observations. Extensive benchmarks exemplify its performance.

A plurality of epidemiological models are analyzed using physics-informed neural networks to identify time-dependent parameters and data-driven fractional models. The results are reported for different geographical locations by inferring unknown parameters and unobserved dynamics.

The authors present a framework for modeling cell interactions using rigid bodies, which can used to represent cells as free moving polygons, to allow epithelial layers to smoothly interact, to model bacteria and to robustly represent membranes.