Individual skill
neural-operator
Train neural operators (FNO, DeepONet) to learn solution maps for parametric PDE families. Once trained, solve new PDE instances in milliseconds. Use when you need to solve many instances of the same PDE with different parameters/ICs/BCs.
backend/cli/skills/physics/neural-operator/SKILL.md
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# Neural Operators (FNO / DeepONet)
## Overview
Neural operators learn mappings between function spaces — given an input function (initial condition, forcing, boundary), they predict the output function (PDE solution). Once trained on a dataset of PDE solutions, they solve new instances in milliseconds.
## When to Use
- You need to solve the SAME PDE many times with different parameters
- Real-time predictions needed (design optimization, control)
- Building a surrogate model for expensive simulations
- The PDE family is known but expensive to solve numerically
## Do NOT Use When