Tony Lelievre (DDMCS@Turing): Coarse-graining stochastic dynamics
Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp
From playlist Data driven modelling of complex systems
Sebastian Reich (DDMCS@Turing): Learning models by making them interact
Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp
From playlist Data driven modelling of complex systems
Eric Vanden-Eijnden (DDMCS@Turing): Neural networks as interacting particle systems
Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp
From playlist Data driven modelling of complex systems
Mathematical modeling of evolving systems
Discover the multidisciplinary nature of the dynamical principles at the core of complexity science. COURSE NUMBER: CAS 522 COURSE TITLE: Dynamical Systems LEVEL: Graduate SCHOOL: School of Complex Adaptive Systems INSTRUCTOR: Enrico Borriello MODE: Online SEMESTER: Fall 2021 SESSION:
From playlist What is complex systems science?
Data Modeling Tutorial | Data Modeling for Data Warehousing | Data Warehousing Tutorial | Edureka
***** Data Warehousing & BI Training: https://www.edureka.co/data-warehousing-and-bi ***** Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations. Therefore, th
From playlist Data Warehousing Tutorial Videos
Jonathan Weare (DDMCS@Turing): Stratification for Markov Chain Monte Carlo
Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp
From playlist Data driven modelling of complex systems
Vincent Danos (DDMCS@Turing): Stability and inference for position-dependent Langevin diffusions
Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp
From playlist Data driven modelling of complex systems
John Murray: "Neural Circuit Modeling of Large-Scale Brain Dynamics for Computational Psychiatry"
Computational Psychiatry 2020 "Neural Circuit Modeling of Large-Scale Brain Dynamics for Computational Psychiatry" John Murray - Yale University Abstract: A critical explanatory gap in clinical neuroscience lies in our mechanistic understanding of how systems-level neuroimaging biomarker
From playlist Computational Psychiatry 2020
Stabilizing Biological Populations: The Experimental Biologist’s Perspective by Sutirth Dey
DISCUSSION MEETING : MATHEMATICAL AND STATISTICAL EXPLORATIONS IN DISEASE MODELLING AND PUBLIC HEALTH ORGANIZERS : Nagasuma Chandra, Martin Lopez-Garcia, Carmen Molina-Paris and Saumyadipta Pyne DATE & TIME : 01 July 2019 to 11 July 2019 VENUE : Madhava Lecture Hall, ICTS, Bangalore
From playlist Mathematical and statistical explorations in disease modelling and public health
James Thorson - Forecasting non-local climate impacts for mobile marine species using extensions...
Dr James Thorson (National Oceanic and Atmospheric Administration) presents "Forecasting non-local climate impacts for mobile marine species using extensions to empirical orthogonal function analysis", 8 May 2020.
From playlist Statistics Across Campuses
ETH Lec 07. Stochastic Growth Models II: Gibrat's vs Yule Simon Model (05/04/2012)
Course: ETH - Collective Dynamics of Firms (Spring 2012) From: ETH Zürich Source: http://www.video.ethz.ch/lectures/d-mtec/2012/spring/363-0543-00L/b0cfc537-1b86-4d4c-88c3-ce932c1156c1.html
From playlist ETH Zürich: Collective Dynamics of Firms (Spring 2012) | CosmoLearning.org Finance
Minyi Huang: "Mean field Stackelberg Games: State Feedback Equilibrium"
High Dimensional Hamilton-Jacobi PDEs 2020 Workshop III: Mean Field Games and Applications "Mean field Stackelberg Games: State Feedback Equilibrium" Minyi Huang - Carleton University Abstract: We study mean field Stackelberg games between a major player (the leader) and a large populati
From playlist High Dimensional Hamilton-Jacobi PDEs 2020
Kristi Morgansen: "Analytical & Empirical Tools for Nonlinear Network Observability in Autonomou..."
Mathematical Challenges and Opportunities for Autonomous Vehicles 2020 Workshop IV: Social Dynamics beyond Vehicle Autonomy "Analytical and Empirical Tools for Nonlinear Network Observability in Autonomous Systems" Kristi Morgansen - University of Washington Abstract: A fundamental eleme
From playlist Mathematical Challenges and Opportunities for Autonomous Vehicles 2020
Pierre Degond (DDMCS@Turing): From micro to macro in collective dynamics
Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp
From playlist Data driven modelling of complex systems
On a local Lyapunov function for the McKean-Vlasov dynamics by Rajesh Sundaresan
Large deviation theory in statistical physics: Recent advances and future challenges DATE: 14 August 2017 to 13 October 2017 VENUE: Madhava Lecture Hall, ICTS, Bengaluru Large deviation theory made its way into statistical physics as a mathematical framework for studying equilibrium syst
From playlist Large deviation theory in statistical physics: Recent advances and future challenges
Networks: Part 3 - Oxford Mathematics 4th Year Student Lecture
Network Science provides generic tools to model and analyse systems in a broad range of disciplines, including biology, computer science and sociology. This course (we are showing the whole course over the next few weeks) aims at providing an introduction to this interdisciplinary field o
From playlist Oxford Mathematics Student Lectures - Networks
SICSS 2017 - Guest Lecture by Sandra Gonzalez-Bailon (Day 4. June 22, 2017)
The first Summer Institute in Computational Social Science was held at Princeton University from June 18 to July 1, 2017, sponsored by the Russell Sage Foundation. For more details, please visit https://compsocialscience.github.io/summer-institute/2017/
From playlist Guest Speakers
Statistical Modeling in Business Analytics with R | Edureka
( R Training : https://www.edureka.co/r-for-analytics ) A statistical model is a formalization of relationships between variables in the form of mathematical equations. A statistical model describes how one or more random variables are related to one or more other variables. The topics in
From playlist R Tutorial Videos
Andrew White: "Maximum Entropy Methods for Combining Physics-Based Simulation with Empirical Data"
Machine Learning for Physics and the Physics of Learning 2019 Workshop II: Interpretable Learning in Physical Sciences "Maximum Entropy Methods for Combining Physics-Based Simulation with Empirical Data" Andrew White, University of Rochester Abstract: Physics-based simulation models like
From playlist Machine Learning for Physics and the Physics of Learning 2019