Mixing Differential Equations and Neural Networks for Physics-Informed Learning
Parallel Computing and Scientific Machine Learning
Mixing Differential Equations and Neural Networks for Physics-Informed Learning
58:59
Uncertainty Programming: Differentiable Programming Extended to Uncertainty Quantification
Parallel Computing and Scientific Machine Learning
Uncertainty Programming: Differentiable Programming Extended to Uncertainty Quantification
47:16
Global Sensitivity Analysis
Parallel Computing and Scientific Machine Learning
Global Sensitivity Analysis
1:19:43
From Optimization to Probabilistic Programming
Parallel Computing and Scientific Machine Learning
From Optimization to Probabilistic Programming
1:46:50
Code Profiling and Optimization (in Julia)
Parallel Computing and Scientific Machine Learning
Code Profiling and Optimization (in Julia)
1:15:44
GPU Programming in Julia
Parallel Computing and Scientific Machine Learning
GPU Programming in Julia
33:43
Parallel Computing: From SIMD to SIMT
Parallel Computing and Scientific Machine Learning
Parallel Computing: From SIMD to SIMT
28:09
Partial Differential Equations (PDEs), Convolutions, and the Mathematics of Locality
Parallel Computing and Scientific Machine Learning
Partial Differential Equations (PDEs), Convolutions, and the Mathematics of Locality
1:38:29
Differentiable Programming Part 2: Adjoint Derivation for (Neural) ODEs and Nonlinear Solve
Parallel Computing and Scientific Machine Learning
Differentiable Programming Part 2: Adjoint Derivation for (Neural) ODEs and Nonlinear Solve
1:36:11
Differentiable Programming Part 1: Reverse-Mode AD Implementation
Parallel Computing and Scientific Machine Learning
Differentiable Programming Part 1: Reverse-Mode AD Implementation
47:58
Basic Parameter Estimation, Reverse-Mode AD, and Inverse Problems
Parallel Computing and Scientific Machine Learning
Basic Parameter Estimation, Reverse-Mode AD, and Inverse Problems
2:16:20
Solving Stiff Ordinary Differential Equations
Parallel Computing and Scientific Machine Learning
Solving Stiff Ordinary Differential Equations
2:21:02
Forward-Mode Automatic Differentiation (AD) via High Dimensional Algebras
Parallel Computing and Scientific Machine Learning
Forward-Mode Automatic Differentiation (AD) via High Dimensional Algebras
1:51:05
Ordinary Differential Equations 2: Discretizations and Stability
Parallel Computing and Scientific Machine Learning
Ordinary Differential Equations 2: Discretizations and Stability
1:31:35
Ordinary Differential Equations 1: Applications and Solution Characteristics
Parallel Computing and Scientific Machine Learning
Ordinary Differential Equations 1: Applications and Solution Characteristics
39:50
The Different Flavors of Parallelism: Parallel Programming Models
Parallel Computing and Scientific Machine Learning
The Different Flavors of Parallelism: Parallel Programming Models
1:51:30
The Basics of Single Node Parallel Computing
Parallel Computing and Scientific Machine Learning
The Basics of Single Node Parallel Computing
1:32:54
How Loops Work 2: Computationally-Efficient Discrete Dynamics
Parallel Computing and Scientific Machine Learning
How Loops Work 2: Computationally-Efficient Discrete Dynamics
48:37
How Loops Work 1: An Introduction to the Theory of Discrete Dynamical Systems
Parallel Computing and Scientific Machine Learning
How Loops Work 1: An Introduction to the Theory of Discrete Dynamical Systems
1:03:51
Introduction to Scientific Machine Learning 1: Deep Learning as Function Approximation
Parallel Computing and Scientific Machine Learning
Introduction to Scientific Machine Learning 1: Deep Learning as Function Approximation
28:22
Introduction to Scientific Machine Learning 2: Physics-Informed Neural Networks
Parallel Computing and Scientific Machine Learning
Introduction to Scientific Machine Learning 2: Physics-Informed Neural Networks
29:00
Optimizing Serial Code in Julia 1: Memory Models, Mutation, and Vectorization
Parallel Computing and Scientific Machine Learning
Optimizing Serial Code in Julia 1: Memory Models, Mutation, and Vectorization
1:20:57
Getting Started with Julia (for Experienced Programmers)
Parallel Computing and Scientific Machine Learning
Getting Started with Julia (for Experienced Programmers)
34:11
Optimizing Serial Code in Julia 2: Type inference, function specialization, and dispatch
Parallel Computing and Scientific Machine Learning
Optimizing Serial Code in Julia 2: Type inference, function specialization, and dispatch
1:25:00
Parallel Computing and Scientific Machine Learning Course: Syllabus
Parallel Computing and Scientific Machine Learning
Parallel Computing and Scientific Machine Learning Course: Syllabus
43:00