Complex Systems Studies

🔸 ICTP wants to sponsor you to the Masters of High Performance Computing! Applications for the ICTP full scholarship for candidates from Developing Countries are now open! Deadline is May 10: http://ictp.it/1fl22

⭕ Looking for a Masters student to work on an MS thesis project on applying machine learning for mental health @TAledavood https://t.co/V5Ivtr77J8

🔸 We have just seen that the complexities of things can so easily and dramatically escape the simplicity of the equations which describe them. Unaware of the scope of simple equations, man has often concluded that nothing short of God, not mere equations, is required to explain the complexities of the world. . . . The next great era of awakening of human intellect may well produce a method of understanding the qualitative content of equations.

Richard Feynman in 1963, the year of publication of the Lorenz model 

R. P. Feynman, R. B. Leighton and M. Sands, The Feynman Lectures on Physics, Vol. II. New York: Addison-Wesley, 1963, Chap. 40, pp. 11, 12.

👀 https://www.quantamagazine.org/physicists-discover-exotic-patterns-of-synchronization-20190404/

🔹 In "complexity economics", people are not purely rational or self-interested, but reason with limited information https://t.co/BGNMXzo58y

🔥 Thesis by Mark Buchanan | The limits of machine prediction https://t.co/6juE011OEs

🚘 "Berlin 8 a.m." - the newest #ComplexityExplorable on traffic dynamics, congestion, and phantom traffic jams. https://t.co/2umN9BcjiP

✅ محمد رضا رحیمی تبار در شپرینگر با کتابی در حوزه فیزیک سیستم های پیچیده دکتر محمدرضا رحیمی تبار استاد دانشکده فیزیک دانشگاه صنعتی شریف کتابی را با عنوان " آنالیز و بازسازی داده محور از سیستم های دینامیکی پیچیده و غیرخطی" Analysis and Data-Based Reconstruction of Complex Nonlinear Dynamical Systems را با انتشارات معتبر و معروف شپرینگر به چاپ رسانده است.دکتر محمدرضا ... 📣 متن کامل را در Instant View ⚡️ (دکمه پایین صفحه) و یا در وب‌گاه انجمن فیزیک ایران بخوانید: 🚩http://www.psi.ir/news2_fa.asp?id=2775 ⏪ وب‌گاه انجمن فیزیک ایران: 🌍 http://www.psi.ir ✅ به کانال خبرى انجمن فیزیک ايران بپيوندید: 👇👇🏽👇👇🏽👇👇🏽👇 http://t.me/psinews

🔥 Can the laws of physics untangle traffic jams, stock markets, and other #complexsystems? https://t.co/UNdXXBvuVI

😺 https://physics.aps.org/articles/v12/35

🎬 https://youtu.be/hR3llUTdefk

🎞 "Introduction to Empirical Dynamic Modeling": a very nice video from the Sugihara Lab "This movie demonstrates the relationship between time series and dynamic attractors" https://t.co/XsrzlSc8BW

🎞 We all intuitively understand what is alive and what is not. Equally intuitively we know that time flows forward and the past is distinct from the future. Yet casting these ideas into a predictive mathematical framework and applying it to understand the unique science of living things has been extraordinarily challenging. We now know that the appropriate language for exploring these questions is the mathematics of chance. In other contexts, such as investment strategies which account for fluctuating stock values, we have had success in using the rules of chance to make profitable predictions. Can we do the same to better understand the laws of life and time?

About the Speaker
Using rapid, iterative feedback between theory and experiments, SFI External Professor Srividya Iyer-Biswas (Purdue University) works to discover the basic physical laws that govern the probabilistic behavior of single cells, and that transcend details of specific biological systems. Her research uses a top-down physics approach, rather than more traditional approaches that focus on the cartography of genetic networks and on molecular details.

Iyer-Biswas and her team have reported predicative scaling laws governing the stochastic growth and division of cells, and have developed a theory that reveals the emergence of a scalable, cellular unit of time. Her current work involves extending these results to thermodynamics of organismal computation, time-dependent phenomena involving cellular decision-making, and laws that dictate complex biological and social phenomena. https://www.aparat.com/v/k4h7e

کارسوق علم‌داده - IPM دوره‌ی کارسوق‌های علم داده تمام ابزار مورد نیاز علم داده در علوم علی‌الخصوص فیزیک را پوشش می‌دهد. این دوره با مباحث پایه آغاز شده و شرکت‌کنندگان در پایان اطلاعات کافی و توانایی حل مسئله خواهند داشت. با توجه به اهمیت این ابزار، فرصت شغلی وسیعتری در انتظار شرکت‌کنندگان خواهد بود. شرکت‌کنندگان حضوری ملزم به انجام تمرینات خواهند بود و در پایان دوره گواهینامه‌ی شرکت دریافت خواهند کرد. ویدئوی کلاس‌ها ضبط و در شبکه‌های عمومی منتشر خواهد شد و افرادی که به طور غیر حضوری در انجام تمرینات شرکت کنند نیز بنا به درخواست گواهی دریافت خواهند کرد. برای هماهنگی شرکت حضوری به آقای علیرضا وفایی‌صدر ایمیل (vafaei.sadr@gmail.com) بزنید. 🔗 اطلاعات بیشتر در: http://physics.ipm.ac.ir/~vafaei/

علیرضا وفایی‌صدر
محقق پسادکتری پژوهشکده‌ی فیزیک، پژوهشگاه دانش‌های بنیادی

🔸 اگر در تهران نيستيد می‌توانيد در كلاس‌های غيرحضوری شركت كنيد!

🔥 Machine learning and the physical sciences

Giuseppe Carleo, Ignacio Cirac, Kyle Cranmer, Laurent Daudet, Maria Schuld, Naftali Tishby, Leslie Vogt-Maranto, Lenka Zdeborová

🔗 https://arxiv.org/pdf/1903.10563.pdf 📌 ABSTRACT Machine learning encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. We review in a selective way the recent research on the interface between machine learning and physical sciences.This includes conceptual developments in machine learning (ML) motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross-fertilization between the two fields. After giving basic notion of machine learning methods and principles, we describe examples of how statistical physics is used to understand methods in ML. We then move to describe applications of ML methods in particle physics and cosmology, quantum many body physics, quantum computing, and chemical and material physics. We also highlight research and development into novel computing architectures aimed at accelerating ML. In each of the sections we describe recent successes as well as domain-specific methodology and challenges.

“Rich data are revealing that complex dependencies between the nodes of a network may not be captured by models based on pairwise interactions. Higher-order network models go beyond these limitations” https://t.co/vYBRPpUIJ7

This algorithm browses Wikipedia to auto-generate textbooks https://t.co/bExALBY6u0 #DeepLearning #MachineLearning #AI #DataScience

#فیلم سخنرانی‌های بیست‌و‌چهارمین مدرسه‌ی فیزیک دکتر غلامرضا جعفری ۲

#فیلم سخنرانی‌های بیست‌و‌چهارمین مدرسه‌ی فیزیک دکتر غلامرضا جعفری ۱