Examples and Results

This page focuses on practical backend runs with expected outputs and validation criteria.

Example 1: RC Step (sanity baseline)

PYTHONPATH=build/python python3 benchmarks/benchmark_runner.py \
  --only rc_step \
  --output-dir benchmarks/out_rc

Expected behavior:

• V(out) rises exponentially.
• very low analytical error for RC waveform.
• low rejection count and no unstable fallback loop.

Example 2: Buck Converter (switching transient)

PYTHONPATH=build/python python3 benchmarks/benchmark_runner.py \
  --only buck_switching \
  --output-dir benchmarks/out_buck

Checkpoints:

• output settles around expected duty-dependent value.
• ripple remains physically plausible for chosen L/C and switching frequency.
• telemetry does not show runaway fallback escalation.

Example 2a: Buck Averaged Pair (switching reference + averaged mode)

PYTHONPATH=build/python python3 benchmarks/benchmark_runner.py \
  --only buck_switching_paired buck_averaged_mvp buck_averaged_expected_failure \
  --output-dir benchmarks/out_buck_averaged

Checkpoints:

• both pair runs (buck_switching_paired, buck_averaged_mvp) are passed.
• buck_averaged_mvp emits max_error from paired_reference validation.
• expected-failure case matches typed diagnostic (AveragedInvalidConfiguration).

Runnable Python example:

PYTHONPATH=build/python python3 examples/run_buck_averaged_mvp.py

Example 2b: AC Sweep RC Low-Pass (frequency-domain baseline)

PYTHONPATH=build/python python3 benchmarks/benchmark_runner.py \
  --only ac_rc_lowpass \
  --output-dir benchmarks/out_ac_rc

Checkpoints:

• output artifact includes frequency_hz, magnitude_db, and phase_deg.
• analytical validation (ac_analytical) remains within configured magnitude/phase thresholds.
• benchmark telemetry exposes ac_sweep_mag_error and ac_sweep_phase_error.

Example 3: Closed-Loop Buck (controller + PWM path)

PYTHONPATH=build/python python3 benchmarks/benchmark_runner.py \
  --only buck_mosfet_nonlinear \
  --output-dir benchmarks/out_cl_buck

Checkpoints:

• duty command remains bounded in [0, 1].
• output tracks reference without persistent divergence.
• control path remains deterministic between runs.

Example 4: Electro-Thermal Scenario

PYTHONPATH=build/python python3 benchmarks/benchmark_runner.py \
  --benchmarks benchmarks/electrothermal_benchmarks.yaml \
  --output-dir benchmarks/out_electrothermal

Checkpoints:

• device temperatures increase consistently with losses.
• thermal_summary.max_temperature remains within design envelope.
• efficiency and loss totals are coherent with operating point.

Example 5: Closed-Loop Buck + Thermal Port Validation

PYTHONPATH=build/python python3 - <<'PY'
import pulsim as ps

parser = ps.YamlParser(ps.YamlParserOptions())
circuit, options = parser.load("examples/09_buck_closed_loop_loss_thermal_validation_backend.yaml")
options.newton_options.num_nodes = int(circuit.num_nodes())
options.newton_options.num_branches = int(circuit.num_branches())

sim = ps.Simulator(circuit, options)
result = sim.run_transient(circuit.initial_state())

print("success:", result.success)
print("steps:", result.total_steps)
print("max_temp:", result.thermal_summary.max_temperature)
for item in result.component_electrothermal:
    if item.thermal_enabled:
        print(item.component_name, item.average_power, item.final_temperature)
PY

Checkpoints:

• closed-loop control remains bounded (PI output and PWM duty).
• thermal-enabled devices report non-empty telemetry in component_electrothermal.
• run remains stable for longer windows (10 ms, 20 ms, ...).

Example 6: Magnetic Core MVP (saturation + frequency-sensitive loss)

PYTHONPATH=build/python python3 examples/run_magnetic_core_saturation_freq_loss.py

Checkpoints:

• Lsat.core_loss exists in result.virtual_channels and stays non-negative.
• metadata for Lsat.core_loss is coherent (domain=loss, unit=W, source_component=Lsat).
• when loss_policy: loss_summary, summary row Lsat.core is present in loss_summary.device_losses.
• average/peak trends are deterministic for repeated runs with the same setup.

Notebook walkthrough:

• examples/notebooks/35_magnetic_core_mvp_tutorial.ipynb

Output Artifacts for Automation

Main files used in CI/regression tooling:

• results.json: benchmark metrics and telemetry summaries
• results.csv: tabular benchmark export
• parity_results.json: external simulator parity metrics
• stress_results.json: tiered stress evaluation

Minimal results.json shape

{
  "benchmark_id": "buck_converter",
  "status": "passed",
  "runtime_s": 0.42,
  "steps": 19876,
  "max_error": 0.0018,
  "telemetry": {
    "newton_iterations": 53211,
    "timestep_rejections": 21,
    "linear_fallbacks": 3
  }
}

Notebook Coverage

Reference notebooks are under examples/notebooks and include:

• first-contact setup
• converter design scenarios
• thermal modeling
• benchmark and validation workflows

See Notebooks for execution details.