Events

Abstract: Consider a biomolecular reaction network that exhibits robust perfect adaptation to disturbances from several parallel sources. The well-known Internal Model Principle of control theory suggests that such systems must include a subsystem (called the “internal model”) that is able to recreate the dynamic structure of the disturbances. This requirement poses certain structural constraints on the network …

We will discuss about "Physics-informed learning of governing equations from scarce data", Chen et al., Nature Communications, 2021 Abstract: Extracting governing equations from data is a central challenge in many diverse areas of science and engineering. Data are abundant whereas models often remain elusive, as in climate science, neuroscience, ecology, finance, and epidemiology, to name …

We will discuss about "RTransferEntropy — Quantifying information flow between different time series using effective transfer entropy", Behrendt et al., SoftwareX, 2019 Abstract: This paper shows how to quantify and test for the information flow between two time series with Shannon transfer entropy and Rényi transfer entropy using the R package RTransferEntropy. We discuss the methodology, the bias …

We will discuss about "A topological data analysis based classifier", Kindelan et al., arXiv, 2022 Abstract: Topological Data Analysis is an emergent field that aims to discover the underlying dataset’s topological information. Topological Data Analysis tools have been commonly used to create filters and topological descriptors to improve Machine Learning (ML) methods. This paper proposes …

We will discuss about "An Efficient Characterization of Complex-Balanced, Detailed-Balanced, and Weakly Reversible Systems", Craciun et al., SIAM Journal on Applied Mathematics, 2020 Abstract: Very often, models in biology, chemistry, physics, and engineering are systems of polynomial or power-law ordinary differential equations, arising from a reaction network. Such dynamical systems can be generated by many …