DDPS | Cheap and robust adaptive reduced order models for nonlinear inversion and design
Description: Nonlinear inverse problems and other PDE-constrained optimization problems, such as structural design under many load cases, require the repeated solution of many discretized large linear systems (or nonlinear systems). For Newton-type methods we also need solutions for the ad
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The regression framework relies on sparsity promoting techniques to select the nonlinear and partial derivative
From playlist Research Abstracts from Brunton Lab
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
A solar system, a simulation made with Excel
An Excel simulation of the solar system. You can see how things are recursively computed: the mutual gravity force from the locations, the accelerations, the velocities, and finally the updated locations. The solar eclipse is also shown. This is clip is intended to illustrate Chapter 24 Ap
From playlist Physics simulations
Lecture 13 - Debugging ML Models and Error Analysis | Stanford CS229: Machine Learning (Autumn 2018)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3E2bjyY Andrew Ng Adjunct Professor of Computer Science https://www.andrewng.org/ To follow along with the course schedule and syllabus, visit: http://cs229.sta
From playlist Stanford CS229: Machine Learning Full Course taught by Andrew Ng | Autumn 2018
DDPS | Non-intrusive reduced order models using physics informed neural networks
The development of reduced order models for complex applications, offering the promise for rapid and accurate evaluation of the output of complex models under parameterized variation, remains a very active research area. Applications are found in problems which require many evaluations, sa
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
DDPS | Deep learning for reduced order modeling
Description: Reduced order modeling (ROM) techniques, such as the reduced basis method, provide nowadays an essential toolbox for the efficient approximation of parametrized differential problems, whenever they must be solved either in real-time, or in several different scenarios. These ta
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
Sparse Nonlinear Dynamics Models with SINDy, Part 5: The Optimization Algorithms
This video discusses the various machine learning optimization schemes that may be used for the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm. We discuss the LASSO sparse regression, sequential thresholded least squares (STLS), and the sparse relaxed regularized regression
From playlist Data-Driven Dynamical Systems with Machine Learning
Valeria Simoncini: Computational methods for large-scale matrix equations and application to PDEs
Linear matrix equations such as the Lyapunov and Sylvester equations and their generalizations have classically played an important role in the analysis of dynamical systems, in control theory and in eigenvalue computation. More recently, matrix equations have emerged as a natural linear a
From playlist Numerical Analysis and Scientific Computing
How to make a viral video -- Day 6
doing a few visualizations. Almost there guys! -- Watch live at https://www.twitch.tv/simuleios
From playlist Viral videos
Stanford Seminar - Evolution of a Web3 Company
This talk was given the week of October 3, 2022. Guest speaker: Sam Green, Co-Founder & Head of Research at Semiotic Labs. #web3
This video discusses how to compute the Discrete Fourier Transform (DFT) matrix in Matlab and Python. In practice, the DFT should usually be computed using the fast Fourier transform (FFT), which will be described in the next video. Book Website: http://databookuw.com Book PDF: http:
From playlist Data-Driven Science and Engineering
Live Stream #129: Jabril visits The Coding Train!
The Coding Train is welcomes Jabril from SEFD Science! Jabril demos his Color Predictor project and answers questions about his journey teaching himself machine learning. đź”— Code presented today: http://sefdstuff.com/Color_NN_Project.zip 2:20 - Intro 14:16 - Guest tutorial: Jabrils 55:55
From playlist Live Stream Archive
Stanford Seminar - Runway: A New Tool for Distributed Systems Design
EE380: Colloquium on Computer Systems Runway: A New Tool for Distributed Systems Design Speaker: Diego Ongaro, Salesforce Distributed systems are notoriously difficult to get right. We're constantly improving the frameworks we use and the way we test production code, yet we rarely inves
From playlist Stanford EE380-Colloquium on Computer Systems - Seminar Series
!!Con 2016 - What Developers and Economists Can Learn from Each Other! By Rob Jefferson
What Developers and Economists Can Learn from Each Other! By Rob Jefferson (Note: This talk was originally scheduled to appear at !!Con 2015.) In the past twenty-ish years, I've had a variety of professional roles: sysadmin, researcher, shipping-container-farm designer, grad student in ec
From playlist !!Con 2016
Space Trees and Stuff! -- Day 7
Alright, barnes hut almost complete! We just need to subdivide the tree consistently! -- Watch live at https://www.twitch.tv/simuleios
From playlist Space trees and stuff
RailsConf 2016 - Tweaking Ruby GC Parameters for Fun, Speed, and Profit by Helio Cola
Tweaking Ruby GC Parameters for Fun, Speed, and Profit by Helio Cola Whether you are building a Robot, controlling a Radar, or creating a Web App, the Ruby Garbage Collector (GC) can help you. The stats exposed by the Garbage Collector since Ruby v2.1 caught my attention and pushed me to
From playlist RailsConf 2016
!!Con 2016 - lol im so random! By Mark Wunsch
lol im so random! By Mark Wunsch Randomness has many applications in computing ranging from cryptography and statistics to generative art and simulation, but where does randomness come from? When you ask for a random number from your system, how truly random is it? This talk will explore
From playlist RailsConf 2016
GRCon19 - Exponent: Arbitrary Bandwidth Receiver Architecture by Dana Sorensen
Exponent: Arbitrary Bandwidth Receiver Architecture by Dana Sorensen, Jake Gunther, Colton Lindstrom This paper presents an architecture for receiving arbitrarily wide bandwidth signals using multiple narrowband receivers. Information contained in overlapping spectral regions provides the
From playlist GRCon 2019
Integration 10 Numerical Integration Video 1 Part 2.mov
Discussion on numerical integration using Trapezoidal Sums.
From playlist Integration