![]() Natsu's Striking Strength and durability are both one tier above Naruto's. Varies if he activates different forms, but Naruto's never faster than him. Naruto may not take an IK with the flames, but since Natsu's emotions translate to the flame's temperature, it may hurt a ton Naruto can't resist getting his soul hurt, which is on Natsu's power list If we take anything close to 30min, we can say that Naruto's practically halfing the lifespam of someone on the same level (imo slightly weaker 1200/2=600/2=300/2=150/2=75/2=37,5) on each hit) (unnoficial calc: considering here, five punches turned 20h in ~30min. ![]() Natsu cannot resist Naruto's lifespam-reducing punches If chakra counts as magic, Natsu could end up absorbing Sage Mode's chakra and becoming a stone, since he can absorb magic to heal(?) Natsu doesn't resist Durability Negation and Power Nullification, which Naruto has. Saiken's Acid would bypass a minor resistance to Poison Naruto has NPI, so Natsu's Incorporeality is unusable Machine Learning for Dynamical Systems: how to analyze dynamical systems on the basis of observed data rather than attempt to study them analytically.ĭynamical Systems for Machine Learning: how to analyze algorithms of Machine Learning using tools from the theory of dynamical systems.Naruto can resist Natsu's flames, since chakra mode v1 resisted Amaterasu (a reference of something really hot on the verse) and we know he's tons of times stronger than this, nowadays, but not so much, since Natsu's Flames are REALLY hot (>200m celsius degrees). The intersection of the fields of dynamical systems and machine learning is largely unexplored, and the goal of this project is to bring together researchers from these fields to fill the gap between the theories of dynamical systems and machine learning in the following directions: This is frequently the case in many systems of interest, and the development of data-driven technologies is becoming increasingly important in many applications. The machine learning approach is invaluable in settings where no explicit model is formulated, but measurement data is available. Applications for machine learning methods include computer vision, stock market analysis, speech recognition, recommender systems and sentiment analysis in social media. On the other hand, the field of machine learning is concerned with algorithms designed to accomplish a certain task, whose performance improves with the input of more data. While models are very precise for many processes, for some of the most challenging applications of dynamical systems (such as climate dynamics, brain dynamics, biological systems or the financial markets), the development of such models is notably difficult. ![]() This deep understanding leads to a model, which is an approximation of the observed reality and is often expressed by a system of Ordinary/Partial, Underdetermined (Control), Deterministic/Stochastic differential or difference equations. From this perspective, the modeling of dynamical processes in applications requires a detailed understanding of the processes to be analyzed. Since its inception in the 19th century through the efforts of Poincaré and Lyapunov, the theory of dynamical systems addresses the qualitative behaviour of dynamical systems as understood from models.
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