Phase plot of twinkling eyes, from Yang et al. 2002
"Spatial Resonances and Superposition Patterns in a Reaction-Diffusion Model with Interacting Turing Modes" (2002) Lingfa Yang, Milos Dolnik, Anatol M. Zhabotinsky, and Irving R. Epstein http://hopf.chem.brandeis.edu/members_content/yanglingfa/paper/t2.pdf The system referenced in Fig. 4.
From playlist Ready
More detail in phase plot of twinkling eyes, from Yang et al. 2002
Plotting chemical 'a' against 'c'. "Spatial Resonances and Superposition Patterns in a Reaction-Diffusion Model with Interacting Turing Modes" (2002) Lingfa Yang, Milos Dolnik, Anatol M. Zhabotinsky, and Irving R. Epstein http://hopf.chem.brandeis.edu/members_content/yanglingfa/paper/t2.p
From playlist Ready
Promoting global stability in data-driven models of quadratic nonlinear dynamics - Trapping SINDy
System identification methods attempt to discover physical models directly from a dataset of measurements, but often there are no guarantees that the resulting models are stable. This video abstract summarizes our recent work that builds in a notion of long-term boundedness (or global stab
From playlist Research Abstracts from Brunton Lab
(ML 7.7) Dirichlet-Categorical model (part 1)
The Dirichlet distribution is a conjugate prior for the Categorical distribution (i.e. a PMF a finite set). We derive the posterior distribution and the (posterior) predictive distribution under this model.
From playlist Machine Learning
(ML 13.6) Graphical model for Bayesian linear regression
As an example, we write down the graphical model for Bayesian linear regression. We introduce the "plate notation", and the convention of shading random variables which are being conditioned on.
From playlist Machine Learning
(ML 7.8) Dirichlet-Categorical model (part 2)
The Dirichlet distribution is a conjugate prior for the Categorical distribution (i.e. a PMF a finite set). We derive the posterior distribution and the (posterior) predictive distribution under this model.
From playlist Machine Learning
Lecture15. Cascades in networks. Influence maximization
Network Science 2021 @ HSE
From playlist Network Science, 2021
Statistical Properties of the Navier-Stokes-Voigt Model by Edriss S. Titi
Program Turbulence: Problems at the Interface of Mathematics and Physics (ONLINE) ORGANIZERS: Uriel Frisch (Observatoire de la Côte d'Azur and CNRS, France), Konstantin Khanin (University of Toronto, Canada) and Rahul Pandit (Indian Institute of Science, Bengaluru) DATE: 07 December 202
From playlist Turbulence: Problems at The Interface of Mathematics and Physics (Online)
Nexus Trimester - Giacomo Como (Lund University)
Resilient control of dynamic flow networks Giacomo Como (Lund University) february 29, 2016 Abstract: This talk focuses on distributed control of dynamical flow networks. These are modeled as dynamical systems derived from mass conservation laws on directed capacitated networks. The flow
From playlist Nexus Trimester - 2016 - Central Workshop
Using MultiStart for Optimization Problems
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Find the best-fit parameters for an exponential model. For more videos, visit http://www.mathworks.com/products/global-optimization/examples.html
From playlist Math, Statistics, and Optimization
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
Francesco Morandin: Turbulence, shell models and critical exponents for dissipation
The lecture was held within the of the Hausdorff Junior Trimester Program: Randomness, PDEs and Nonlinear Fluctuations. Abstract: Shell models of turbulence are nonlinear dynamical systems inspired by fluid dynamics. They are idealized and simplified, but tailored to exhibit the same ener
From playlist HIM Lectures: Junior Trimester Program "Randomness, PDEs and Nonlinear Fluctuations"
How stable is the earth’s climate? (Lecture 1) by J Srinivasan
DISCUSSION MEETING: WORKSHOP ON CLIMATE STUDIES (HYBRID) ORGANIZERS: Rama Govindarajan (ICTS-TIFR, India), Sandeep Juneja (TIFR, India), Ramalingam Saravanan (Texas A&M University, USA) and Sandip Trivedi (TIFR, India) DATE : 01 March 2022 to 03 March 2022 VENUE: Ramanujan Lecture Hall
From playlist Workshop on Climate Studies - 2022
Dynamics of waves and vortices in the ocean by Jim Thomas
ICTS Seminar Title : Dynamics of waves and vortices in the ocean Speaker : Jim Thomas (University of North Carolina at Chapel Hill, United States) Date : Monday, 2nd November,
From playlist Seminar Series
Professor Stéphane Mallat: "High-Dimensional Learning and Deep Neural Networks"
The Turing Lectures: Mathematics - Professor Stéphane Mallat: High-Dimensional Learning and Deep Neural Networks Click the below timestamps to navigate the video. 00:00:07 Welcome by Professor Andrew Blake, Director, The Alan Turing Institute 00:01:36 Introduction by Professo
From playlist Turing Lectures
Lecture 5 (CEM) -- TMM Using Scattering Matrices
This lecture formulates a stable transfer matrix method based on scattering matrices. The scattering matrices adopted here are greatly improved from the literature and are consistent with convention. The lecture ends with some advanced topics like dispersion analysis, cascading and doubl
From playlist UT El Paso: CEM Lectures | CosmoLearning.org Electrical Engineering
Energy Spectra and Fluxes in Buoyant Flows by Mahendra Kumar Verma
DISCUSSION MEETING WAVES, INSTABILITIES AND MIXING IN ROTATING AND STRATIFIED FLOWS (ONLINE) ORGANIZERS: Thierry Dauxois (CNRS & ENS de Lyon, France), Sylvain Joubaud (ENS de Lyon, France), Manikandan Mathur (IIT Madras, India), Philippe Odier (ENS de Lyon, France) and Anubhab Roy (IIT M
From playlist Waves, Instabilities and Mixing in Rotating and Stratified Flows (ONLINE)
(ML 10.2) Posterior for linear regression (part 1)
How to compute the posterior distribution for the weight vector w under a Bayesian model for linear regression.
From playlist Machine Learning