We will discuss about “Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems”, Linka, Kevin, et al., Computer Methods in Applied Mechanics and Engineering Volume 402, 1 December 2022, 115346 Abstract Understanding real-world dynamical phenomena remains a challenging task. Across various scientific disciplines, machine learning has advanced as the go-to technology to analyze nonlinear dynamical …

We will discuss about “Topological Data Analysis in Time Series: Temporal Filtration and Application to Single-Cell Genomics" , Lin, Baihan., arXiv preprint arXiv:2204.14048 (2022). Abstract The absence of a conventional association between the cell-cell cohabitation and its emergent dynamics into cliques during development has hindered our understanding of how cell populations proliferate, differentiate, and compete, i.e. …

We will discuss about “A scalable approach for solving chemical master equations based on modularization and filtering ”, Fang, Zhou, Ankit Gupta, and Mustafa Khammash., bioRxiv (2022). Abstract Solving the chemical master equation (CME) that characterizes the probability evolution of stochastically reacting processes is greatly important for analyzing intracellular reaction systems. Conventional methods for solving CMEs …

We will discuss about “Optimal information networks: Application for data-driven integrated health in populations”, Servadio, Joseph L., and Matteo Convertino, Science Advances 4.2 (2018): e1701088. Abstract Development of composite indicators for integrated health in populations typically relies on a priori assumptions rather than model-free, data-driven evidence. Traditional variable selection processes tend not to consider relatedness and …

We will discuss about “Estimating and Assessing Differential Equation Models with Time-Course Data”, Wong, Samuel WK, Shihao Yang, and S. C. Kou, arXiv preprint arXiv:2212.10653 (2022). Abstract Ordinary differential equation (ODE) models are widely used to describe chemical or biological processes. This article considers the estimation and assessment of such models on the basis of time-course …