The most commonly used stiff ODE solver isn't A-stable?!?!?
Chris Rackauckas
The most commonly used stiff ODE solver isn't A-stable?!?!?
2:24
Physics-informed neural networks without learning boundary conditions!?!?!
Chris Rackauckas
Physics-informed neural networks without learning boundary conditions!?!?!
1:20
There are ODEs for which Euler's Method is more efficient!
Chris Rackauckas
There are ODEs for which Euler's Method is more efficient!
1:12
Scientific machine learning and heterogeneous data
Chris Rackauckas
Scientific machine learning and heterogeneous data
1:37
Free type-stability via world-splitting optimizations in #julialang
Chris Rackauckas
Free type-stability via world-splitting optimizations in #julialang
1:06
Automatic differentiation is incorrect on very simple functions??? 😱 😱 😱
Chris Rackauckas
Automatic differentiation is incorrect on very simple functions??? 😱 😱 😱
1:40
Runge-Kutta's 4th order method is not optimal
Chris Rackauckas
Runge-Kutta's 4th order method is not optimal
1:02
Should Programming Languages Change for LLMs like Claude and ChatGPT? #julialang #vibecoding
Chris Rackauckas
Should Programming Languages Change for LLMs like Claude and ChatGPT? #julialang #vibecoding
1:54
Why Floating Point Accuracy Isn't Enough for Stable Algorithms
Chris Rackauckas
Why Floating Point Accuracy Isn't Enough for Stable Algorithms
1:26
Automatic Differentiation is not Efficient on Newton's Method
Chris Rackauckas
Automatic Differentiation is not Efficient on Newton's Method
0:59
The Numerical Analysis of Differentiable Simulation: Automatic Differentiation Can Be Incorrect
Chris Rackauckas
The Numerical Analysis of Differentiable Simulation: Automatic Differentiation Can Be Incorrect
1:07:51
Automatic differentiation | New derivations of nonlinear solve and ODE adjoints
Chris Rackauckas
Automatic differentiation | New derivations of nonlinear solve and ODE adjoints
1:19:25
What is (scientific) machine learning? An introduction through Julia's Lux.jl
Chris Rackauckas
What is (scientific) machine learning? An introduction through Julia's Lux.jl
1:18:56
What is machine learning and how can it be connected to prior scientific knowledge (SciML)?
Chris Rackauckas
What is machine learning and how can it be connected to prior scientific knowledge (SciML)?
55:33
Model Discovery w/ Imposed Structures and Prior Knowledge Scientific Machine Learning | ML4Science
Chris Rackauckas
Model Discovery w/ Imposed Structures and Prior Knowledge Scientific Machine Learning | ML4Science
1:13:36
Fast Neural ODE / UDE: Improved Parallelism and Memory Performance Differentiating Stiff ODEs
Chris Rackauckas
Fast Neural ODE / UDE: Improved Parallelism and Memory Performance Differentiating Stiff ODEs
26:50
Extending Scientific Machine Learning (SciML) to Agent-Based Models (ICLR AI4ABM 2023)
Chris Rackauckas
Extending Scientific Machine Learning (SciML) to Agent-Based Models (ICLR AI4ABM 2023)
25:50
Introduction to Scientific Machine Learning in Astroinformatics Part 2: Numerics
Chris Rackauckas
Introduction to Scientific Machine Learning in Astroinformatics Part 2: Numerics
49:52
Introduction to Scientific Machine Learning in Astroinformatics Part 1: Applications
Chris Rackauckas
Introduction to Scientific Machine Learning in Astroinformatics Part 1: Applications
39:26
SciML Open Source Software Organization One Minute Pitch
Chris Rackauckas
SciML Open Source Software Organization One Minute Pitch
1:00
A Comparison of Automatic Differentiation and Adjoints for Derivatives of Differential Equations
Chris Rackauckas
A Comparison of Automatic Differentiation and Adjoints for Derivatives of Differential Equations
12:07
Pharmacometrics-Informed Deep Learning with DeepNLME - ISCB 2021 Invited Session
Chris Rackauckas
Pharmacometrics-Informed Deep Learning with DeepNLME - ISCB 2021 Invited Session
31:32
Opening the Blackbox: Accelerating Neural Differential Equations (ICML 2021)
Chris Rackauckas
Opening the Blackbox: Accelerating Neural Differential Equations (ICML 2021)
4:52
Symbolics.jl - High performance symbolic numerics via multiple dispatch, Julia Computer Algebra
Chris Rackauckas
Symbolics.jl - High performance symbolic numerics via multiple dispatch, Julia Computer Algebra
16:12
Accelerated Large-Eddy Simulation via Universal Partial Differential Equations
Chris Rackauckas
Accelerated Large-Eddy Simulation via Universal Partial Differential Equations
20:35
Accelerating Quantitative Systems Pharmacology with Machine Learning - SMB 2021
Chris Rackauckas
Accelerating Quantitative Systems Pharmacology with Machine Learning - SMB 2021
15:05
Scientific Machine Learning and Stiffness - MIT Institute for AI and Fundamental Interactions IAIFI
Chris Rackauckas
Scientific Machine Learning and Stiffness - MIT Institute for AI and Fundamental Interactions IAIFI
1:02:53
Stiffness in Scientific Machine Learning: Cornell SCAN Seminar
Chris Rackauckas
Stiffness in Scientific Machine Learning: Cornell SCAN Seminar
47:25
Automated Discovery of Mechanistic Models via Universal Differential Equations
Chris Rackauckas
Automated Discovery of Mechanistic Models via Universal Differential Equations
10:51
GPU Acceleration of Quantitative Systems Pharmacology (QSP) Workflows
Chris Rackauckas
GPU Acceleration of Quantitative Systems Pharmacology (QSP) Workflows
10:00
Differential Equations in 2021
Chris Rackauckas
Differential Equations in 2021
1:25:30
Stability-Optimized High Order Methods for Pathwise Stiffness in Stochastic Differential Equations
Chris Rackauckas
Stability-Optimized High Order Methods for Pathwise Stiffness in Stochastic Differential Equations
11:33
Scientific Machine Learning (SciML) Helicopter Challenge Problem
Chris Rackauckas
Scientific Machine Learning (SciML) Helicopter Challenge Problem
4:08
COVID-19 Epidemic Mitigation via Scientific Machine Learning (SciML)
Chris Rackauckas
COVID-19 Epidemic Mitigation via Scientific Machine Learning (SciML)
52:10
Cheap But Effective: Instituting Effective Pandemic Policies Without Knowing Who's Infected (SciML)
Chris Rackauckas
Cheap But Effective: Instituting Effective Pandemic Policies Without Knowing Who's Infected (SciML)
14:37
Universal Differential Equations for SciML - Modeling and Computation Seminar, Chris Rackauckas
Chris Rackauckas
Universal Differential Equations for SciML - Modeling and Computation Seminar, Chris Rackauckas
1:08:00
Universal Differential Equations for Scientific Machine Learning - Chris Rackauckas MIT
Chris Rackauckas
Universal Differential Equations for Scientific Machine Learning - Chris Rackauckas MIT
58:56
Julia and DifferentialEquations.jl : Chris Rackauckas, MIT
Chris Rackauckas
Julia and DifferentialEquations.jl : Chris Rackauckas, MIT
39:02
Simulation and Control of Biological Stochasticity - Chris Rackauckas PhD Defense
Chris Rackauckas
Simulation and Control of Biological Stochasticity - Chris Rackauckas PhD Defense
1:24:35
Dense or Continuous Output for ODE Solvers
Chris Rackauckas
Dense or Continuous Output for ODE Solvers
12:47
Runge-Kutta Methods for ODEs
Chris Rackauckas
Runge-Kutta Methods for ODEs
12:17
Using Juno for Interactive Test-Driven Julia Package Development
Chris Rackauckas
Using Juno for Interactive Test-Driven Julia Package Development
24:15
Quick Overview of DiffEqOperators.jl for Contributors / GSoC (February 2018)
Chris Rackauckas
Quick Overview of DiffEqOperators.jl for Contributors / GSoC (February 2018)
1:34:59
DifferentialEquations.jl: A performant and feature-rich ecosystem for solving differential equations
Chris Rackauckas
DifferentialEquations.jl: A performant and feature-rich ecosystem for solving differential equations
8:18
Developing and Editing Julia Packages
Chris Rackauckas
Developing and Editing Julia Packages
48:27