Time Stepping Skill | Agent Skills

Time Stepping

Goal

Provide a reliable workflow for choosing, ramping, and monitoring time steps plus output/checkpoint cadence.

Requirements

Python 3.8+
No external dependencies (uses stdlib)

Inputs to Gather

| Input | Description | Example | |-------|-------------|---------| | Stability limits | CFL/Fourier/reaction limits | dt_max = 1e-4 | | Target dt | Desired time step | 1e-5 | | Total run time | Simulation duration | 10 s | | Output interval | Time between outputs | 0.1 s | | Checkpoint cost | Time to write checkpoint | 120 s |

Decision Guidance

Time Step Selection

Is stability limit known?
├── YES → Use min(dt_target, dt_limit × safety)
└── NO → Start conservative, increase adaptively

Need ramping for startup?
├── YES → Start at dt_init, ramp to dt_target over N steps
└── NO → Use dt_target from start

Ramping Strategy

| Problem Type | Ramp Steps | Initial dt | |--------------|------------|------------| | Smooth IC | None needed | Full dt | | Sharp gradients | 5-10 | 0.1 × dt | | Phase change | 10-20 | 0.01 × dt | | Cold start | 10-50 | 0.001 × dt |

Script Outputs (JSON Fields)

| Script | Key Outputs | |--------|-------------| | scripts/timestep_planner.py | dt_limit, dt_recommended, ramp_schedule | | scripts/output_schedule.py | output_times, interval, count | | scripts/checkpoint_planner.py | checkpoint_interval, checkpoints, overhead_fraction |

Workflow

Get stability limits - Use numerical-stability skill
Plan time stepping - Run scripts/timestep_planner.py
Schedule outputs - Run scripts/output_schedule.py
Plan checkpoints - Run scripts/checkpoint_planner.py
Monitor during run - Adjust dt if limits change

Conversational Workflow Example

User: I'm running a 10-hour phase-field simulation. How often should I checkpoint?

Agent workflow:

Plan checkpoints based on acceptable lost work:

python3 scripts/checkpoint_planner.py --run-time 36000 --checkpoint-cost 120 --max-lost-time 1800 --json

Interpret: Checkpoint every 30 minutes, overhead ~0.7%, max 30 min lost work on crash.

Pre-Run Checklist

[ ] Confirm dt limits from stability analysis
[ ] Define ramping strategy for transient startup
[ ] Choose output interval consistent with physics time scales
[ ] Plan checkpoints based on restart risk
[ ] Re-evaluate dt after parameter changes

CLI Examples

# Plan time stepping with ramping
python3 scripts/timestep_planner.py --dt-target 1e-4 --dt-limit 2e-4 --safety 0.8 --ramp-steps 10 --json

# Schedule output times
python3 scripts/output_schedule.py --t-start 0 --t-end 10 --interval 0.1 --json

# Plan checkpoints for long run
python3 scripts/checkpoint_planner.py --run-time 36000 --checkpoint-cost 120 --max-lost-time 1800 --json

Error Handling

| Error | Cause | Resolution | |-------|-------|------------| | dt-target must be positive | Invalid time step | Use positive value | | t-end must be > t-start | Invalid time range | Check time bounds | | checkpoint-cost must be < run-time | Checkpoint too expensive | Reduce checkpoint size |

Interpretation Guidance

dt Behavior

| Observation | Meaning | Action | |-------------|---------|--------| | dt stable at target | Good | Continue | | dt shrinking | Stability issue | Check CFL, reduce target | | dt oscillating | Borderline stability | Add safety factor |

Checkpoint Overhead

| Overhead | Acceptability | |----------|---------------| | < 1% | Excellent | | 1-5% | Good | | 5-10% | Acceptable | | > 10% | Too frequent, increase interval |

Limitations

Not adaptive control: Plans static schedules, not runtime adaptation
Assumes constant physics: If parameters change, re-plan

References

references/cfl_coupling.md - Combining multiple stability limits
references/ramping_strategies.md - Startup policies
references/output_checkpoint_guidelines.md - Cadence rules

Version History

v1.1.0 (2024-12-24): Enhanced documentation, decision guidance, examples
v1.0.0: Initial release with 3 planning scripts

Agent Skills: Time Stepping

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