Dynamic Data Driven Applications Systems (DDDAS) is a new paradigm whereby the computation and instrumentation aspects of an application system are dynamically integrated in a feed-back control loop, in the sense that instrumentation data can be dynamically incorporated into the executing model of the application, and in reverse the executing model can control the instrumentation. Such approaches have been shown that can enable more accurate and faster modeling and analysis of the characteristics and behaviors of a system and can exploit data in intelligent ways to convert them to new capabilities, including decision support systems with the accuracy of full scale modeling, efficient data collection, management, and data mining. The DDDAS concept - and the term - was proposed by Frederica Darema for the National Science Foundation (NSF) workshop in March 2000. There are several affiliated annual meetings and conferences, including: * DDDAS workshop at ICCS (since 2003) * DyDESS conference and workshop at MIT organized by Sai Ravela and Adrian Sandu * DDDAS special session at the ACC organized by Puneet Singla and Dennis Bernstein and Sai Ravela * DDDAS Special Session Information Fusion (Wikipedia).
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)
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
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
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)