Agent skill
devito
Symbolic PDE solver with automatic code generation for finite-difference computations. Use when Claude needs to: (1) Perform seismic wave propagation modeling, (2) Implement acoustic or elastic wave equations, (3) Run forward modeling for shot gathers, (4) Set up Full Waveform Inversion (FWI) workflows, (5) Implement Reverse Time Migration (RTM), (6) Create absorbing boundary conditions, (7) Generate optimized stencil code for CPUs/GPUs, (8) Solve custom PDEs with finite differences.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/devito
SKILL.md
Devito - Symbolic PDE Solver
Quick Reference
from devito import Grid, Function, TimeFunction, Eq, solve, Operator
# Create grid
grid = Grid(shape=(101, 101), extent=(1000., 1000.))
# Velocity model
v = Function(name='v', grid=grid, space_order=4)
v.data[:] = 1500.
# Wavefield
p = TimeFunction(name='p', grid=grid, time_order=2, space_order=4)
# Wave equation: d2p/dt2 = v^2 * laplacian(p)
stencil = Eq(p.forward, solve(p.dt2 - v**2 * p.laplace, p.forward))
# Compile and run
op = Operator([stencil])
op(time_M=100, dt=0.5)
Key Classes
| Class | Purpose |
|---|---|
Grid |
Computational domain definition |
Function |
Spatial field on grid |
TimeFunction |
Time-dependent field |
SparseTimeFunction |
Point sources/receivers |
Operator |
Compiled computation kernel |
Essential Operations
Grid and Fields
from devito import Grid, Function, TimeFunction
# 2D/3D Grid
grid = Grid(shape=(nx, nz), extent=(x_size, z_size))
# Velocity model (spatial field)
v = Function(name='v', grid=grid, space_order=4)
v.data[:] = 1500.
# Wavefield (time-dependent)
p = TimeFunction(name='p', grid=grid, time_order=2, space_order=4)
Source and Receivers
from examples.seismic import RickerSource, Receiver, TimeAxis
time_range = TimeAxis(start=0., stop=1000., step=dt)
# Source
src = RickerSource(name='src', grid=grid, f0=10., npoint=1, time_range=time_range)
src.coordinates.data[0, :] = [500., 20.]
# Receivers
rec = Receiver(name='rec', grid=grid, npoint=101, time_range=time_range)
rec.coordinates.data[:, 0] = np.linspace(0., 1000., 101)
rec.coordinates.data[:, 1] = 20.
Build and Run
# Wave equation
stencil = Eq(p.forward, solve(p.dt2 - v**2 * p.laplace, p.forward))
src_term = src.inject(field=p.forward, expr=src * dt**2 * v**2)
rec_term = rec.interpolate(expr=p)
# Compile and execute
op = Operator([stencil] + src_term + rec_term)
op(time_M=nt-1, dt=dt)
# Results
shot_record = rec.data # (nt, nrec)
snapshot = p.data[0] # Current wavefield
Symbolic Derivatives
| Syntax | Description |
|---|---|
p.dt, p.dt2 |
First/second time derivative |
p.dx, p.dy, p.dz |
Spatial derivatives |
p.laplace |
Laplacian (auto-adapts to dims) |
p.forward |
p at t+dt (time stepping) |
p.backward |
p at t-dt (adjoint) |
Stability and Accuracy
CFL Condition: dt < dx / (v_max * sqrt(ndim))
| Dims | Max dt |
|---|---|
| 2D | dx / (v_max * 1.414) |
| 3D | dx / (v_max * 1.732) |
| Space Order | Stencil Points | Error |
|---|---|---|
| 2 | 3 | O(h^2) |
| 4 | 5 | O(h^4) |
| 8 | 9 | O(h^8) |
Higher order = more accurate but slower. Use 4-8 for production.
When to Use vs Alternatives
| Scenario | Recommendation |
|---|---|
| Seismic wave propagation (acoustic/elastic) | Devito - symbolic PDE, auto-optimized code |
| Full Waveform Inversion (FWI) or RTM | Devito - adjoint support, GPU-ready |
| Legacy seismic processing pipelines | Madagascar - established, large script library |
| Simple 1D/2D wave demos | Custom NumPy - no dependencies, easier to debug |
| General-purpose PDE solving (non-wave) | FEniCS - FEM-based, broader PDE support |
| Production seismic imaging at scale | Devito - generates optimized C code, MPI support |
Choose Devito when: You need high-performance finite-difference wave propagation with symbolic equation specification. It auto-generates optimized C/OpenMP/GPU code from Python-level math, making it ideal for FWI, RTM, and research prototyping.
Avoid Devito when: You need finite-element methods (use FEniCS), or simple pedagogical examples where NumPy suffices.
Common Workflows
Acoustic wave forward modelling with sources and receivers
- Define
Gridwith shape and physical extent matching the velocity model - Create velocity
Functionand populate with model values - Create
TimeFunctionfor the wavefield (time_order=2, space_order=4+) - Verify CFL condition:
dt < dx / (v_max * sqrt(ndim)) - Build wave equation stencil:
Eq(p.forward, solve(p.dt2 - v**2 * p.laplace, p.forward)) - Create source (
RickerSource) and receivers, set coordinates - Add source injection and receiver interpolation terms
- Compile
Operatorwith stencil + source + receiver terms - Run operator:
op(time_M=nt-1, dt=dt) - Extract shot record from
rec.dataand plot
References
- Operators and Stencils - Detailed operator construction
- Performance Optimization - GPU execution and tuning
Scripts
- scripts/acoustic_wave.py - Basic acoustic wave modeling
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