New tutorial: Partitioned Burgers eq. 1D #670
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… in visualize_partitioned_domain.py. Wrap boundary conditions in new class BoundaryWrapper because they are dependent on the solver iterations. Implement implicit Euler (and Jacobian and residual for the root finding iteration. Initialize participants. Zero gradient condition on the outside boundaries.
vidulejs
changed the title
…-surrogate.sh, generate-training-data.sh. Fix floating point issue in t_index. Add --savefile argument to solver-scipy-fvolumes.py to save data for training.
…n scripts. Fix requirements.txt for scipy and surrogate solvers. Add example images for README
…ipt to check for initial conditions after installing environment.
uekerman
reviewed
Oct 21, 2025
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| title: Partitioned 1D Burgers' Equation | |||
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| title: Partitioned 1D Burgers' Equation | |
| title: Partitioned Burgers' equation 1D |
| - **Dirichlet**: Solves the left half $[0,1]$ and receives Dirichlet boundary conditions at the interface. | ||
| - **Neumann**: Solves the right half $[1,2]$ and receives Neumann boundary conditions at the interface. | ||
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| Both outer boundaries use zero-gradient conditions $\frac{\partial u}{\partial x} = 0$. The problem is solved for different initial conditions of superimposed sine waves, which can be generated using the provided script. |
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Which script, how/when do I need to run it?
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| This tutorial includes two versions for the Neumann participant: | ||
| - A standard finite volume solver (`neumann-scipy`). | ||
| - A pre-trained neural network surrogate that approximates the solver (`neumann-surrogate`). |
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This information goes into subsection "Available solvers"
| </p> | ||
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| ## Running the tutorial |
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| ## Running the tutorial | |
| ## Running the simulation |
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| ```bash | ||
| python3 generate_ic.py --epoch <seed> | ||
| ``` |
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Is this really a script that is general to all solvers? (otherwise, put in subfolders)
still, a tools subfolder could be good`
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| # sol = solve_ivp(wrapper.rhs, (t, t_end), u, method='BDF', t_eval=[t_end], jac=wrapper.jac) # BDF higher order implicit timestepping | ||
| # u = sol.y[:, -1] | ||
| # u = u + dt * burgers_rhs(t, u, dx, C_viscosity, wrapper.bc_left(u), wrapper.bc_right(u)) # Explicit Euler |
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Do we need this commented-out code? If yes, could we make this configurable (i.e. testable)?
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| <!-- <acceleration:constant> | ||
| <relaxation value="0.01" /> | ||
| </acceleration:constant> --> |
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| <!-- <acceleration:constant> | |
| <relaxation value="0.01" /> | |
| </acceleration:constant> --> |
| . ../../tools/log.sh | ||
| exec > >(tee --append "$LOGFILE") 2>&1 | ||
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| echo "[preCICE] Waiting for Dirichlet participant..." |
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No need to log this. preCICE should do this, no?
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| rm -rf precice-profiling | ||
| rm -f neumann-surrogate.log precice-Neumann-convergence.log precice-Neumann-iterations.log | ||
| rm -f surrogate.npz No newline at end of file |
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Compare how other tutorials do this.
| set -e -u | ||
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| rm -f solver-scipy/full_domain.npz | ||
| rm -f images/full_domain_evolution.png images/full_domain_timestep_slice.png images/gradient_timestep_slice.png images/initial_condition.png |
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move those results to output/?
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New tutorial case: Partitioned Burgers' Equation in 1D solved with SciPy Finite Volume implementation and a Neural Network surrogate model !
Checklist:
changelog-entries/<PRnumber>.md.