Automated Discovery of Mechanistic Models via Universal Differential Equations
For the Applied Biomath 2020 QSP Summit
https://www.appliedbiomath.com/quantitative-systems-pharmacology-summit-2020
SciML Scientific Machine Learning
https://sciml.ai/
Find more at:
StochasticLifestyle.com
Видео Automated Discovery of Mechanistic Models via Universal Differential Equations канала Christopher Rackauckas
https://www.appliedbiomath.com/quantitative-systems-pharmacology-summit-2020
SciML Scientific Machine Learning
https://sciml.ai/
Find more at:
StochasticLifestyle.com
Видео Automated Discovery of Mechanistic Models via Universal Differential Equations канала Christopher Rackauckas
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Universal Differential Equations for SciML - Modeling and Computation Seminar, Chris Rackauckas
How Do Physics-Informed Neural Networks Work?
Deconvolution | Image Processing II
What are Differential Equations and how do they work?
Introducing Weird Differential Equations: Delay, Fractional, Integro, Stochastic!
Differential Equations in 2021
JuliaCon 2019 | Neural Ordinary Differential Equations with DiffEqFlux | Jesse Bettencourt
What is Automatic Differentiation?
Partial Derivatives and the Gradient of a Function
AN20: Partial Differential Equations Meet Deep Learning: Old Solutions for New Problems & Vice Versa
Bayesian Neural Ordinary Differential Equations | Raj Dandekar | JuliaCon2021
Purple Presentation: Fractional Derivatives & Fractional Fourier Transforms
Linear Systems: Matrix Methods | MIT 18.03SC Differential Equations, Fall 2011
Fractional physics informed neural networks, by Prof. George Karniadakis & Mr. Liu Yang
COVID-19 Epidemic Mitigation via Scientific Machine Learning (SciML)
Risi Kondor: "Fourier space neural networks"
Differentiable Physics (for Deep Learning), Overview Talk by Nils Thuerey
Neural Differential Equations
The stability of equilibria of a differential equation, analytic approach
This Neural Network Turns Videos Into 60 FPS!