Prerequisite chain
Prerequisites for Physics-Informed Neural Networks
Topics you need before working through Physics-Informed Neural Networks. Direct prerequisites are listed first; transitive prerequisites (the chain reachable through them) follow.
Direct prerequisites (4)
- The Jacobian Matrixlayer 0A, tier 1
- Automatic Differentiationlayer 1, tier 1
- Feedforward Networks and Backpropagationlayer 2, tier 1
- Gradient Descent Variantslayer 1, tier 1
Reachable through the chain (12)
These topics are not directly cited as prerequisites but are reached transitively by following the chain upward. Working through the direct prerequisites pulls these in.
- Differentiation in Rnlayer 0A, tier 1
- Sets, Functions, and Relationslayer 0A, tier 1
- Basic Logic and Proof Techniqueslayer 0A, tier 2
- Vectors, Matrices, and Linear Mapslayer 0A, tier 1
- Continuity in Rⁿlayer 0A, tier 1
- Metric Spaces, Convergence, and Completenesslayer 0A, tier 1
- Matrix Calculuslayer 1, tier 1
- The Hessian Matrixlayer 0A, tier 1
- Matrix Operations and Propertieslayer 0A, tier 1
- Eigenvalues and Eigenvectorslayer 0A, tier 1
- Activation Functionslayer 1, tier 1
- Convex Optimization Basicslayer 1, tier 1