Dynamic Data Driven Applications Systems

Data-Driven Dynamical Systems Overview

This video provides a high-level overview of this new series on data-driven dynamical systems. In particular, we explore the various challenges in modern dynamical systems, along with emerging techniques in data science and machine learning to tackle them. The two chief challenges are 1)

From playlist Data-Driven Dynamical Systems with Machine Learning

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

System Identification: Full-State Models with Control

This lecture provides an overview of modern data-driven regression methods for linear and nonlinear system identification, based on the dynamic mode decomposition (DMD), Koopman theory, and the sparse identification of nonlinear dynamics (SINDy). https://www.eigensteve.com/

From playlist Data-Driven Control with Machine Learning

Discrete-Time Dynamical Systems

This video shows how discrete-time dynamical systems may be induced from continuous-time systems. https://www.eigensteve.com/

From playlist Data-Driven Dynamical Systems

Data-Driven Control: Linear System Identification

Overview lecture on linear system identification and model reduction. This lecture discusses how we obtain reduced-order models from data that optimally capture input--output dynamics. https://www.eigensteve.com/

From playlist Data-Driven Control with Machine Learning

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

Dynamic Mode Decomposition (Overview)

In this video, we introduce the dynamic mode decomposition (DMD), a recent technique to extract spatio-temporal coherent structures directly from high-dimensional data. DMD has been widely applied to systems in fluid dynamics, disease modeling, finance, neuroscience, plasma physics, robot

From playlist Data-Driven Dynamical Systems with Machine Learning

Formal verification and learning of complex systems - Professor Alessandro Abate

For slides, future Logic events and more, please visit: https://logic-data-science.github.io/?page=logic_learning Two known shortcomings of standard techniques in formal verification are the limited capability to provide system-level assertions, and the scalability to large-scale, complex

From playlist Logic and learning workshop

HLCS | Interpretable and Explainable Data-Driven Methods for Physical Simulations

Description: A data-driven model can be built to accurately accelerate computationally expensive physical simulations, which is essential in multi-query problems, such as uncertainty quantification, design optimization, optimal control, and inverse problems. It is important to build interp

From playlist Hartree–Livermore Computational Science (HLCS)

Data-Driven Control: Overview

Overview lecture for series on data-driven control. In this lecture, we discuss how machine learning optimization can be used to discover models and effective controllers directly from data. Book Website: http://databookuw.com Book PDF: http://databookuw.com/databook.pdf These lectur

From playlist Data-Driven Control with Machine Learning

DDPS | Deep Learning Meets Data Assimilation by Ashesh Chattopadhyay (Rice University)

Description: Our weather or climate system is a high-dimensional, multi-scale, chaotic, dynamical system which presents a challenging problem for fully data-driven models to perform forecasting. Any forecasting pipeline requires the data-driven model to integrate information from the obser

From playlist Data-driven Physical Simulations (DDPS) Seminar Series

Discussion Meeting

PROGRAM: Nonlinear filtering and data assimilation DATES: Wednesday 08 Jan, 2014 - Saturday 11 Jan, 2014 VENUE: ICTS-TIFR, IISc Campus, Bangalore LINK:http://www.icts.res.in/discussion_meeting/NFDA2014/ The applications of the framework of filtering theory to the problem of data assimi

From playlist Nonlinear filtering and data assimilation

Creating HEV Plant Models

Learn about HEV modeling and simulation. In this video, you will: - Learn about different methods for creating HEV component models. - See how Powertrain Blockset™ and Simscape™ tools can be used for HEV modeling. - Learn best practices for getting started and creating new plant models.

From playlist Hybrid Electric Vehicles

Deep Learning to Discover Coordinates for Dynamics: Autoencoders & Physics Informed Machine Learning

Joint work with Nathan Kutz: https://www.youtube.com/channel/UCoUOaSVYkTV6W4uLvxvgiFA Discovering physical laws and governing dynamical systems is often enabled by first learning a new coordinate system where the dynamics become simple. This is true for the heliocentric Copernican syste

From playlist Data-Driven Dynamical Systems with Machine Learning

DDPS | libROM: Library for physics-constrained data-driven physical simulations | Youngsoo Choi

A data-driven model can be built to accurately accelerate computationally expensive physical simulations, which is essential in multi-query problems, such as inverse problem, uncertainty quantification, design optimization, and optimal control. In this talk, two types of data-driven mode

From playlist Data-driven Physical Simulations (DDPS) Seminar Series

Dynamic Mode Decomposition (Examples)

In this video, we continue to explore the dynamic mode decomposition (DMD). In particular, we look at recent methodological extensions and application areas in fluid dynamics, disease modeling, neuroscience, and multiscale physics. http://dmdbook.com/ https://www.eigensteve.com/

From playlist Data-Driven Dynamical Systems with Machine Learning

Data-driven and data-augmented modeling of chaotic spatiotemporal dynamical system by Jaideep Pathak

DISCUSSION MEETING NEUROSCIENCE, DATA SCIENCE AND DYNAMICS (ONLINE) ORGANIZERS: Amit Apte (IISER-Pune, India), Neelima Gupte (IIT-Madras, India) and Ramakrishna Ramaswamy (IIT-Delhi, India) DATE : 07 February 2022 to 10 February 2022 VENUE: Online This discussion meeting on Neuroscien

From playlist Neuroscience, Data Science and Dynamics (ONLINE)