Highlights: Dr. Nassim Nicolas Taleb’s Introduction on Complexity Theory

A Grand Challenge in Biology
Bruce Alberts
http://collinslab.mit.edu/files/science_editorial.pdf

🎯 Introduction to Statistical Mechanics
Peter Eastman

🔗 https://web.stanford.edu/~peastman/statmech/index.html

A very handy resource for Stat. Mech. from Standford University, freely distributed under the terms of the Creative Commons Attribution-NoDerivatives 4.0 International License. The source can be found at https://github.com/peastman/statmech.

💻 Teaching Statistical Physics by Thinking about Models and Algorithms

Jan Tobochnik and Harvey Gould (2008)

📓 Teaching the renormalization group
Humphrey J. Maris and Leo P. KadanoffView Affiliations

American Journal of Physics 46, 652 (1978)

💡 The renormalization group theory of second‐order phase transitions is described in a form suitable for presentation as part of an undergraduate statistical physics course.

💻 A comprehensive simulation on the #Ising_Model by Asher Preska Steinberg, Michael Kosowsky, and Seth Fraden, Physics Department, Brandeis University

🔑 The goal of this experiment was to create #Monte_Carlo simulations of the 1D and 2D #Ising model.
To accomplish this the #Metropolis algorithm was implemented in #MATLAB. The dependence of #magnetization on #temperature with and without an external field was calculated, as well as the dependence of the #energy, #specific_heat, and #magnetic_susceptibility on temperature. The results of the 2D simulation were compared to the #Onsager solution.

🔗 http://fraden.brandeis.edu/courses/phys39/simulations/AsherIsingModelReport.pdf

🍔🍟🍕 https://www.soodabehmilanlouei.com/single-post/2017/07/29/Why-some-foods-go-well-together-and-some-not

🗞 Hyperbolic Geometry of Kuramoto Oscillator Networks

Bolun Chen, Jan R. Engelbrecht, Renato Mirollo

🔗 https://arxiv.org/pdf/1707.00713

📌 ABSTRACT
Kuramoto oscillator networks have the special property that their trajectories are constrained to lie on the (at most) 3D orbits of the M\"obius group acting on the state space TN(the N-fold torus). This result has been used to explain the existence of the N−3 constants of motion discovered by Watanabe and Strogatz for Kuramoto oscillator networks. In this work we investigate geometric consequences of this M\"obius group action. The dynamics of Kuramoto phase models can be further reduced to 2D reduced group orbits, which have a natural geometry equivalent to the unit disk Δ with the hyperbolic metric. We show that in this metric the original Kuramoto phase model (with order parameter Z1 equal to the centroid of the oscillator configuration of points on the unit circle) is a gradient flow and the model with order parameter iZ1(corresponding to cosine phase coupling) is a completely integrable Hamiltonian flow. We give necessary and sufficient conditions for general Kuramoto phase models to be gradient or Hamiltonian flows in this metric. This allows us to identify several new infinite families of hyperbolic gradient or Hamiltonian Kuramoto oscillator networks which therefore have simple dynamics with respect to this geometry. We prove that for the Z1 model, a generic 2D reduced group orbit has a unique fixed point corresponding to the hyperbolic barycenter of the oscillator configuration, and therefore the dynamics are equivalent on different generic reduced group orbits. This is not always the case for more general hyperbolic gradient or Hamiltonian flows; the reduced group orbits may have multiple fixed points, which also may bifurcate as the reduced group orbits vary.

💥 #کارسوق «پیچیدگی های طبیعت»

شرکت برای همه آزاد است. اطلاعات بیشتر و ثبت نام:
http://heliofizx.ir/workshops/complexities-of-nature/
پشتیبانی :
@Heliosupport1
@Heliosupport2

@Helliphysicsclub

دهمین کنفرانس فیزیک آماری (ماده چگال نرم و سیستمهای پیچیده) - 5 و 6 اردیبهشت ماه 1397
www.psi.ir
کانال اطلاع رسانی انجمن فیزیک ایران
psinews@

بعضی از درختان بعد از اینک به صورت کامل رشد کردند و شاخ و برگشان به درختان اطراف خود رسید با رفتاری عجیب از تماس با درختان اطراف خود امتناع می کنند !


@NationalGeographicChannel

The Perfect Swarm: The Science of Complexity in Everyday Life

http://www.lenfisherscience.com/books/the-perfect-swarm-the-science-of-complexity-in-everyday-life/

🔖 Starling Flock Networks Manage Uncertainty in Consensus at Low Cost

George F. Young, Luca Scardovi, Andrea Cavagna, Irene Giardina, Naomi E. Leonard

🔗 http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002894
Published: January 31, 2013

📌 ABSTRACT
Flocks of starlings exhibit a remarkable ability to maintain cohesion as a group in highly uncertain environments and with limited, noisy information. Recent work demonstrated that individual starlings within large flocks respond to a fixed number of nearest neighbors, but until now it was not understood why this number is seven. We analyze robustness to uncertainty of consensus in empirical data from multiple starling flocks and show that the flock interaction networks with six or seven neighbors optimize the trade-off between group cohesion and individual effort. We can distinguish these numbers of neighbors from fewer or greater numbers using our systems-theoretic approach to measuring robustness of interaction networks as a function of the network structure, i.e., who is sensing whom. The metric quantifies the disagreement within the network due to disturbances and noise during consensus behavior and can be evaluated over a parameterized family of hypothesized sensing strategies (here the parameter is number of neighbors). We use this approach to further show that for the range of flocks studied the optimal number of neighbors does not depend on the number of birds within a flock; rather, it depends on the shape, notably the thickness, of the flock. The results suggest that robustness to uncertainty may have been a factor in the evolution of flocking for starlings. More generally, our results elucidate the role of the interaction network on uncertainty management in collective behavior, and motivate the application of our approach to other biological networks.

📍 Author Summary
Starling flocks move in beautiful ways that both captivate and intrigue the observer. Previous work has shown that starlings pay attention to their seven closest neighbors, but until now it was not understood why this number is seven. Our paper explains the mystery: when uncertainty in sensing is present, interacting with six or seven neighbors optimizes the balance between group cohesiveness and individual effort. To prove this result we develop a new systems-theoretic approach for understanding noisy consensus dynamics. The approach allows the evaluation of robustness over a family of hypothesized sensing strategies using observations of the spatial positions of birds within the flock. We apply this approach to experimental data from wild starling flocks, and find that six or seven neighbors yield maximal robustness. The implication that robustness of cohesion may have been a factor in the evolution of flocking has significant consequences for evolutionary biology. In addition, the results and the versatility of the approach have implications for uncertainty management in social and technological networks.

Political Systems in an Age of Complexity

💡آیا ارتباطی بین شکل رود نیل و سطح خورشید وجود دارد؟
در نخستین کارسوق پیچیدگی‌های طبیعت به این پرسش پاسخ خواهیم داد!
@Helliphysicsclub
ثبت نام:

http://heliofizx.ir/workshops/complexities-of-nature/

⛱ نخستین کارسوق پیچیدگی در ایران
ویژه دانش آموزان و دانشجویان کارشناسی
سه شنبه، ۱۷ مرداد، دبیرستان علامه حلی۱ تهران

☀️ چرا پیچیدگی مهم است؟!
با وجود تمام پیشرفت‌های خارق‌العاده بشر در فیزیک و سایر علوم، کماکان در توجیه بسیاری از پدیده‌ها مانده‌ایم. پدیده‌هایی که همیشه اطرافمان حاضر بوده‌اند. بنابراین، می‌توان اینگونه اندیشید که شاید در نگاه ما به طبیعت و مسائل علمی، نقصی وجود داشته باشد. به‌ عبارت دیگر، بعید نیست که مجددا نیاز به بازنگری در نگاهمان به طبیعت (تغییر پارادایم) داشته باشیم؛ عده‌ی زیادی معتقدند آن‌چه که در قرن ۲۱ام نیاز است، نگاهی جدید به مبانی علم است؛ نگاه پیچیدگی!

@Helliphysicsclub
🤔 #سيستم_پيچيده چيست؟
سيستم های پيچيده نگاهی نو به پديده هاي است که به علت ارتباط بين اجزای آن و همچنين ارتباط با ديگر پديده ها، از پيچيدگی بالايی برخوردارند و رفتار جمعی متفاوتی بروز می دهند. بدين معنی که با مطالعه تک تک اجزای یک سيستم پيچيده نمی توان به #رفتار_جمعی آن دست يافت. به عبارت ديگر، سیستم پیچیده معرف انگاره #پیچیدگی است كه عناصر سازنده آن تشكیل شبكه ای را می دهند كه اجزاء شبكه دارای برهم كنش هستند و از اندیشه کل نگر بهره می گيرند. پارادایم کلاسيک كه بخشی نگر است، بر اين فرض استوار است كه اگر اجزاء سیستمی را دقیقاً شناسایی كنیم و از عمل كرد آن اطلاع يابيم، قادر خواهیم بود به خواص كلی پدیده و سیستم دست یابیم. مطالعات كیهان شناسی، ساختارهای بی نظم در مواد، زیست شناسی، جامعه شناسی و اقتصاد محدودیت كاربرد این مسئله در مقالات و كتب متعدد را به نمايش گذاشته اند.


🕸🕸🕸🕸🕸🕸🕸🕸🕸
🦋 شرکت برای همه آزاد است. اطلاعات بیشتر و ثبت نام:
http://heliofizx.ir/workshops/complexities-of-nature/
🦋پشتیبانی :
@Heliosupport1
@Heliosupport2

@Helliphysicsclub

💡چه ارتباطی بین شبکه فیس بوک و شبکه برهمکنش پروتئین-پروتئین وجود دارد؟
در نخستین کارسوق پیچیدگی‌های طبیعت به این پرسش پاسخ خواهیم داد!
ثبت نام:
http://heliofizx.ir/workshops/complexities-of-nature/

🐋 در نخستین کارسوق «پیچیدگی‌های طبیعت»:
🐠 چگونه می‌توان حرکت این ماهی‌ها و کوسه‌ها را به کمک ریاضیات توصیف کرد؟

ثبت نام برای عموم علاقمندان، مستقل از سن، جنسیت و مدرک تحصیلی آزاد است:
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