• Home
  • Team
  • Services
  • Research
  • Publications
  • Institutes
  • Software
  • Simulations
  • Tutorials
  • Educational Materials
  • Posts
  • Podcasts
  • Contact
  • Home
  • Team
  • Services
  • Research
  • Publications
  • Institutes
  • Software
  • Simulations
  • Tutorials
  • Educational Materials
  • Posts
  • Podcasts
  • Contact

Complexity Research Team (CRT)

"Chaos is the beginning, simplicity is the end.", M.C. Escher

Skip to content

Month: November 2020

Professor Robert Sapolsky: “Chaos and Reductionism”

Posted on 25/11/202008/03/2022 by karaka
Posted in Chaos, Educational Tutorials, Physics Tagged Chaos, Reductionism, Robert Sapolsky, Stanford University

Professor Constantino Tsallis “Knowledge and Uncertainty in Physics – Foundations and Applications”

Posted on 25/11/202008/03/2022 by karaka
Posted in Educational Tutorials, Physics, Tsallis q-extensive statistics Tagged Constantino Tsallis, EBICC 2017

Professor Georgios Pavlos: “Complexity and Unification in Physical Theory”

Posted on 19/11/202008/03/2022 by karaka
Posted in Chaos, Educational Tutorials, Physics, Tsallis q-extensive statistics Tagged Complexity, Georgios Pavlos, Physical Theory, Unification

Machine learning analysis of chaos and vice versa

Posted on 12/11/202008/03/2022 by karaka
Posted in Chaos, Educational Tutorials, Machine Learning, Physics Tagged Chaos, Dynamical Systems, Machine Learning

Neural Networks – Deep Learning

Posted on 12/11/202008/03/2022 by karaka
Posted in Deep Learning, Educational Tutorials, Neural Networks

Complexity Theory Courses

Posted on 12/11/202008/03/2022 by karaka
Posted in Educational Tutorials, Physics

Complexity theory, time series analysis and Tsallis q-entropy principle part one: theoretical aspects

Posted on 12/11/202008/03/2022 by karaka

https://www.degruyter.com/downloadpdf/journals/jmbm/26/5-6/article-p139.xml

Posted in Physics, Tsallis q-extensive statistics Tagged Complexity theory, timeseries analysis, Tsallis q-entropy principle

(PINNs) – Physics Informed Neural Networks: Algorithms, Theory, and Applications

Posted on 11/11/202008/03/2022 by karaka
Posted in Machine Learning, Physics Tagged Machine Learning, Neural Networks, Physics

Recent Posts

  • Spatial constrains and information content of sub-genomic regions of the human genome 11/09/2022
  • Special Issue “Coexistence of Complexity Metrics and Machine-Learning Approaches for Understanding Complex Biological Phenomena” 15/12/2020
  • Professor Robert Sapolsky: “Chaos and Reductionism” 25/11/2020

RSS Machine Learning

  • DOE selects MIT to establish a Center for the Exascale Simulation of Coupled High-Enthalpy Fluid–Solid Interactions 10/09/2025
    The research center, sponsored by the DOE’s National Nuclear Security Administration, will advance the simulation of extreme environments, such as those in hypersonic flight and atmospheric reentry.
    Institute for Soldier Nanotechnologies
  • AI and machine learning for engineering design 07/09/2025
    Popular mechanical engineering course applies machine learning and AI theory to real-world engineering design.
    Anne Wilson | Department of Mechanical Engineering
  • A greener way to 3D print stronger stuff 04/09/2025
    MIT CSAIL researchers developed SustainaPrint, a system that reinforces only the weakest zones of eco-friendly 3D prints, achieving strong results with less plastic.
    Rachel Gordon | MIT CSAIL

RSS Biology and Genetics

  • This MIT spinout is taking biomolecule storage out of the freezer 12/09/2025
    Cache DNA has developed technologies that can preserve biomolecules at room temperature to make storing and transporting samples less expensive and more reliable.
    Zach Winn | MIT News
  • Study explains how a rare gene variant contributes to Alzheimer’s disease 10/09/2025
    Lipid metabolism and cell membrane function can be disrupted in the neurons of people who carry rare variants of ABCA7.
    Anne Trafton | MIT News
  • Study finds cell memory can be more like a dimmer dial than an on/off switch 09/09/2025
    The findings may redefine how cell identity is established and enable the creation of more sophisticated engineered tissues.
    Jennifer Chu | MIT News

Machine Learning Mastery

  • The Roadmap for Mastering AI-Assisted Coding in 2025
  • 10 Common Misconceptions About Large Language Models
  • Multi-Agent Systems: The Next Frontier in AI-Driven Cyber Defense
  • ROC AUC vs Precision-Recall for Imbalanced Data
  • 7 Scikit-learn Tricks for Optimized Cross-Validation

Useful Menu

  • Log in
  • Entries feed
  • Comments feed
  • WordPress.org

Statistics

  • 0
  • 792
  • 9,304
  • 1,573

Archives

  • September 2022 (1)
  • December 2020 (1)
  • November 2020 (8)

Categories

  • Biology
  • Chaos
  • Deep Learning
  • Dynamics
  • Educational Tutorials
  • Machine Learning
  • Neural Networks
  • Physics
  • Software
  • Tsallis q-extensive statistics

Pages

  • Contact
  • Educational Materials
  • Home
  • Institutes
  • Podcasts
  • Posts
  • Publications
  • Research
  • Services
  • Simulations
  • Software & Scripts(Software, R, Python)
  • Team
  • Tutorials
Powered by WordPress

All rights reserved © Complexity Research Team (CRT) Theme by Seos Themes