Parallel Computing and Scientific Machine Learning
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Mixing Differential Equations and Neural Networks for Physics-Informed Learning
Uncertainty Programming: Differentiable Programming Extended to Uncertainty Quantification
Global Sensitivity Analysis
From Optimization to Probabilistic Programming
Code Profiling and Optimization (in Julia)
GPU Programming in Julia
Parallel Computing: From SIMD to SIMT
Partial Differential Equations (PDEs), Convolutions, and the Mathematics of Locality
Differentiable Programming Part 2: Adjoint Derivation for (Neural) ODEs and Nonlinear Solve
Differentiable Programming Part 1: Reverse-Mode AD Implementation
Basic Parameter Estimation, Reverse-Mode AD, and Inverse Problems
Solving Stiff Ordinary Differential Equations
Forward-Mode Automatic Differentiation (AD) via High Dimensional Algebras
Ordinary Differential Equations 2: Discretizations and Stability
Ordinary Differential Equations 1: Applications and Solution Characteristics
The Different Flavors of Parallelism: Parallel Programming Models
The Basics of Single Node Parallel Computing
How Loops Work 2: Computationally-Efficient Discrete Dynamics
How Loops Work 1: An Introduction to the Theory of Discrete Dynamical Systems
Introduction to Scientific Machine Learning 1: Deep Learning as Function Approximation
Introduction to Scientific Machine Learning 2: Physics-Informed Neural Networks
Optimizing Serial Code in Julia 1: Memory Models, Mutation, and Vectorization
Getting Started with Julia (for Experienced Programmers)
Optimizing Serial Code in Julia 2: Type inference, function specialization, and dispatch
Parallel Computing and Scientific Machine Learning Course: Syllabus