Physikalische Mathematik

Physics-based image formation models enable computationally obtaining meaningful information by processing other forms of information which can be acquired through measurements. In practical situations however, the inner functionalities of the system which create the impulse response function are usually unknown, and due to noise, measurements are unreliable. Before Deep Neural Networks (DNNs) taking over, Compressed Sensing (CS) techniques were primarily being used to address this lack of information by imposing assumptions into the problem. But this switch to DNNs came with the price of mass data acquisition for training to leap over the never-ending problem of algorithmic fidelity in CS methods. Recently, deep image prior and untrained or semi-trained networks, while leveraging the power of DNNs and algorithms, have become successful to be considered as potential answers to the desire of finding a cost-efficient yet powerful solution. In this paper, we briefly have a look at the recent breakthroughs conducted over this concept to solve various imaging problems.

Numerical models are a valuable apparatus for researching an enormous number of inquiries in digestion, hereditary qualities, and quality climate communications. A model dependent on the fundamental science and organic chemistry is a stage for in silico organic experimentation that can uncover the causal chain of occasions that associate variety in one amount to variety in another [1]. We talk about how we build such models, how we have utilized them to research homeostatic systems, quality climate associations, and genotype-aggregate planning, and how they can be utilized in accuracy and customized medication.

Polynomial math at the further developed level is regularly depicted as current or dynamic polynomial math. Truth be told, both of these depictions are mostly deceptive. A portion of the extraordinary disclosures in the upper compasses of present-day polynomial math for instance, the alleged Galois hypothesis were known numerous years prior to the American Civil War; and the wide points of variable based math today were obviously expressed by Leibniz in the seventeenth century [1]. Consequently, "current" variable based math isn't so exceptionally present day, all things considered how much is it conceptual All things considered, deliberation is all relative; one individual's deliberation is someone else's meat and potatoes. The theoretical propensity in math is similar to the circumstance of changing good codes, or changing preferences for music: What shocks one age turns into the standard in the following.

In this article it is tried to construct a stochastic model which looks a generalized stochastic version of Von Bertalanffy power law model and Richard’s model and one can use to describe biological growth phenomena according to the appropriate situation and suitability of this model. It is mainly constructed to explain growth dynamics of patients infected by COVID-19 in South Korea. Here it is attempted to find the expression of variable of interest at time t and also the MLE of growth rate parameter is worked out. This model is applied to a real life data of infected patients by COVID-19 in South Korea after observing the growth pattern. This model could be used to the data sets of other countries, where no lockdown was imposed as a precautionary measure to deal with this situation. Then a comparative study is made between some well-known models and special cases of the model, described here. It is found that the special cases of the model that is described in this article fits better to the data than others.