We will discuss about “PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations”, Zhong, Weiheng, and Hadi Meidani, Computer Methods in Applied Mechanics and Engineering 403 (2023): 115664. Abstract We propose a new class of physics-informed neural networks, called the Physics-Informed Variational Auto-Encoder (PI-VAE), to solve stochastic differential equations (SDEs) or inverse problems involving SDEs. In …

We will discuss about “Detecting critical state before phase transition of complex biological systems by hidden Markov model”, Chen, Pei, et al. Bioinformatics 32.14 (2016): 2143-2150.   Abstract Motivation: Identifying the critical state or pre-transition state just before the occurrence of a phase transition is a challenging task, because the state of the system may …

We will discuss about “Multiparameter persistent homology landscapes identify immune cell spatial patterns in tumors”, Vipond, Oliver, et al, Proceedings of the National Academy of Sciences 118.41 (2021): e2102166118. Abstract Highly resolved spatial data of complex systems encode rich and nonlinear information. Quantification of heterogeneous and noisy data—often with outliers, artifacts, and mislabeled points—such as …

We will discuss about "Role of DNA binding sites and slow unbinding kinetics in titration-based oscillators”, Karapetyan, Sargis, and Nicolas E. Buchler,Physical Review E 92.6 (2015): 062712. Abstract Genetic oscillators, such as circadian clocks, are constantly perturbed by molecular noise arising from the small number of molecules involved in gene regulation. One of the strongest …

We will discuss about “Optimal control of aging in complex networks”, Sun, Eric D., Thomas CT Michaels, and L. Mahadevan, Proceedings of the National Academy of Sciences 117.34 (2020): 20404-20410. Abstract Many complex systems experience damage accumulation, which leads to aging, manifest as an increasing probability of system collapse with time. This naturally raises the question …