pulsim 1.4.0

High-performance circuit simulator for power electronics

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Header-only C++23 + Python power-electronics simulator with an in-house sparse-LU kernel.

Docs · Tutorials · API reference · How Pulsim works · Examples · Changelog

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TL;DR

Pulsim is a SPICE alternative tuned for the switched-mode power electronics workload: PWL devices, finite-state topologies, $10^8$-step transients. Where SPICE pays full assembly + LU factorization on every step, Pulsim caches one state-space per switch combination and updates only the columns of $L+U$ that actually changed.

The v1.3.0 sparse LU rewrite + the v1.4.0 generalised path-based update framework give:

Workload	n_state	Speedup vs baseline
Buck single-bit Gray-code	14	2.81×
Multi-bit δ=2 switch transition	14	1.58× vs Eigen LU
Parametric sweep (R/L/C)	14–26	3.0–3.7× vs rebuild
AC sweep complex LU	8–32	rough parity with Eigen

Both single-bit and (v1.4.0+) multi-bit / parametric paths run through an in-house C++23 sparse LU. No KLU, no SuiteSparse, no Eigen::SparseLU<complex> in production — only Eigen as a matrix container.

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Why Pulsim

Feature	What it buys you
PLECS-style PWL cache (Layer 4)	switched-converter transient in milliseconds, not minutes
Header-only C++23 kernel	drop pulsim/ into your CMake via pulsim::core; no static-library link
In-house sparse LU (Pulsim's own, v1.3.0+)	path-based partial refactor for switch flips + parameter sweeps; zero third-party LU
Python-first ergonomics	CircuitBuilder takes string node names + SI units; one SimulationResult for transients, AC, sweeps, MC
Mixed-domain control chain	PID / comparator / rate-limiter / FOC / thermal at kernel speed (no Python interpreter per step)
Frequency-domain analysis	small-signal MNA Bode + swept-sine FRA + closed-loop GM/PM in the same surface
Drop-in parameter sweep + MC (v1.4.0+)	sweep_path_aware / monte_carlo_path_aware reuse the cached LU across all sweep points

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Performance

Numbers captured 2026-05-24 on macOS / Apple Silicon (-O3 -DNDEBUG). Full writeups in artigos/02_tpel_methods/benchmarks/; reproducible via cmake --build build --target pulsim_benchmarks.

1) Single-bit rank-1 (v1.3.0, N-switch chain)

3-backend microbench. (A) baseline solve per-mask cache, (B) Eigen sliding solver, (C) Pulsim path-based partial refactor.

N	n_state	µs/solve (A)	µs/eigen (B)	µs/pulsim (C)	C/A
4	6	6.74	6.15	2.30	2.93×
12	14	10.01	5.41	3.56	2.81×
16	18	12.15	7.08	4.31	2.82×
20	22	13.85	8.24	5.08	2.73×
24	26	16.41	9.83	6.13	2.68×

Zero fallbacks across all 1999 single-bit Gray-code flips × 8 N values.

2) Multi-bit path-union (v1.4.0, Pulsim ÷ Eigen sliding solver)

N (n_state)	δ=1	δ=2	δ=3	δ=4
8 (10)	3.12×	1.62×	1.61×	1.42×
12 (14)	1.72×	1.58×	1.58×	1.42×
16 (18)	1.56×	1.28×	1.51×	1.25×
20 (22)	1.36×	1.42×	1.54×	1.51×
24 (26)	1.55×	1.46×	1.33×	1.42×

Hit rate decays gracefully: ~45 % path-union at δ=2 → ~10 % at δ=4 (rest fall back cleanly to full factorize).

3) Parametric sweep / Monte Carlo (v1.4.0, Pulsim ÷ legacy rebuild)

n_state	50 pts	100 pts	500 pts	1000 pts
8	5.18×	3.29×	3.55×	3.68×
14	3.57×	3.02×	3.51×	3.35×
26	3.53×	3.31×	3.38×	3.40×

Zero fallbacks across all 12 cells.

4) AC sweep complex LU (v1.4.0, Pulsim ÷ Eigen)

n	µs/freq Eigen	µs/freq Pulsim	Pulsim ÷ Eigen	Parity Δ
8	8.98	5.48	0.61× (Pulsim faster)	1×10⁻²²
16	8.16	7.64	0.94×	5×10⁻²²
32	13.98	14.56	1.04×	3×10⁻²¹
64	28.49	33.36	1.17×	1×10⁻²¹
128	46.13	91.52	1.98× (Eigen faster)	4×10⁻²¹

Both solvers numerically interchangeable. v1.4.0 contribution is no third-party LU on the production path; Eigen kept explicitly as the IEEE TPEL §VI.B paper-comparison baseline.

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Quick start

Prerequisites

Two native dependencies — everything else is header-only or vendored at configure time:

Dependency	Why
Eigen 3.4+	Header-only sparse linear algebra (matrix + vector containers). Pulsim ships its own sparse LU on top.
C++23 compiler	AppleClang 15+ / Clang 17+ / GCC 13+

# macOS (Homebrew)
brew install cmake ninja eigen

# Debian / Ubuntu
sudo apt-get install -y cmake ninja-build libeigen3-dev

# Fedora
sudo dnf install cmake ninja-build eigen3-devel

Install from PyPI

pip install pulsim

Or build from source

git clone https://github.com/lgili/Pulsim.git
cd Pulsim
pip install -e .

First simulation — 8 lines

import pulsim as p

b = p.CircuitBuilder()
b.add_voltage_source("Vin", "n0", "gnd", 5.0)
b.add_resistor      ("R1", "n0", "vc",   1000.0)
b.add_capacitor     ("C1", "vc", "gnd",  1e-6)

res = p.simulate(b, t_end=5e-3, dt=1e-5)
p.plot.scope(b, res, signals=["vc"])      # one-line waveform

Parametric sweep — exploit the v1.4.0 path-based refactor

import numpy as np
import pulsim as p

def make_rc(R=5.0, C=10e-6):
    b = p.CircuitBuilder()
    b.add_voltage_source("v1", "vin", "gnd", 10.0)
    b.add_resistor      ("R_load", "vin", "vout", R)
    b.add_capacitor     ("C_out", "vout", "gnd", C)
    return b

def steady_state_vout(res, _params):
    states = np.asarray(res.states)
    return {"vout": float(states[-1, 1])}

# Sweep R_load through 100 values. v1.4.0 path-based refactor:
# build the cache ONCE, then reuse the L+U factors at each point.
out = p.sweep_path_aware(
    make_rc(),                               # initial builder
    params={"R_load": np.linspace(1, 20, 100).tolist()},
    kpi_fn=steady_state_vout,
    t_end=5e-3, dt=1e-6,
)
print(out.to_dataframe().head())

For Monte Carlo, swap sweep_path_aware → monte_carlo_path_aware(builder, distributions={"R_load": lambda r: r.uniform(1, 20)}, n_samples=1000, ...) and get the same ~3.4× speedup over the legacy rebuild-per-sample pattern.

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What's new in v1.4.0

Two algorithmic contributions ship in one release — see CHANGELOG.md for the full breakdown.

In-house complex sparse LU

PulsimSparseLuSolver is now templated on Scalar. The new PulsimComplexSparseLuSolver = PulsimSparseLuSolverT<std::complex<Real>> drives the AC-sweep production path — Eigen::SparseLU<complex> is no longer compiled in. Backward-compat aliases keep the v1.3.0 real-scalar API source-identical:

using PulsimSparseLuSolver        = PulsimSparseLuSolverT<Real>;
using PulsimComplexSparseLuSolver = PulsimSparseLuSolverT<std::complex<Real>>;
using Matrix                      = MatrixT<Real>;
using Vector                      = VectorT<Real>;

Multi-bit + parametric path-based update

The v1.3.0 single-bit path-based partial refactor now generalises to three SMPS-relevant use cases:

Case	Mechanism	Speedup
Single-bit switch flip (v1.3.0)	etree path of one column	2.7–2.9×
Multi-bit switch flip (v1.4.0+)	union of etree paths	1.3–1.7×
Parametric value sweep (v1.4.0+)	path of param-affected cols	3.0–3.7×

The MAX_PATH_LENGTH_RATIO = 0.6 gate skips the path-based attempt when the union path covers > 60 % of $n$ (the path-walk would cost the same as a fresh factorise).

Python helpers

Drop-in replacements for the legacy sweep / MC APIs:

import pulsim as p
p.sweep_path_aware(builder, params={...}, kpi_fn=..., t_end=..., dt=...)
p.monte_carlo_path_aware(builder, distributions={...}, n_samples=..., ...)

Auto-fallback to the legacy code path when a parameter name isn't recognised (warns + delegates) — drop-in safe.

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What's NOT in Pulsim (intentionally)

Many simulators inherit a sprawling dependency tree from their sparse-LU backend. Pulsim deliberately doesn't:

❌ Not used	Why
SuiteSparse / KLU	The v1.3.0 in-house LU replaces KLU completely. No SuiteSparse linkage, no licensing complexity.
Eigen::SparseLU	Replaced in v1.4.0 by the in-house complex specialisation. Backend::Eigen is retained explicitly as the IEEE TPEL paper-comparison baseline.
dpsim / dwf / PSCAD vendored code	Pulsim's algorithms are entirely first-party; the methods paper claims ours, not someone else's.
Runtime Python in kernel	The C++ kernel has zero Python dependency. pybind11 only crosses the boundary at the API surface.
Heavy installer / multi-step build	pip install pulsim or cmake --build; no make-then-make-install dance.

What IS used: Eigen 3.4+ (header-only, as a sparse matrix container) and pybind11 (for the Python binding). That's it.

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Architecture

10 layers, bottom-up:

Layer 0:  Sparse LU + matrix containers      (in-house, real + complex)
Layer 1:  Topology graph + switch state mask
Layer 2:  Device models (R, L, C, MOSFET, IGBT, source, ...)
Layer 3:  Symbolic stamping helpers (Newton refresh)
Layer 4:  PWL state-space cache  ← refactor_parametric + solve_rank1
Layer 5:  Solver loop + event detection + run_transient
Layer 6:  CircuitBuilder (ergonomic API surface)
Layer 7:  YAML loader + reference circuits
Layer 8:  AC analysis (small-signal MNA Bode + FRA)
Layer 9:  Python facade (CircuitBuilder, simulate, sweep, plot)

See docs/how-pulsim-works/ for the chapter-by-chapter walkthrough (18 figures, 11 chapters, MNA basics through path-based partial refactor with full provenance).

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Validation

# C++ kernel tests (Catch2 binaries per layer)
ctest --test-dir build --output-on-failure
# 498 / 498 tests pass on a fresh checkout

# Python runtime tests
PYTHONPATH=build/python pytest python/tests -v
# 6 / 6 path-aware sweep tests pass

# Benchmark suite (opt-in, not in default ctest)
./build/core/pulsim_benchmarks "[rank1][microbench]"
./build/core/pulsim_benchmarks "[multi_bit][microbench]"
./build/core/pulsim_benchmarks "[parametric][microbench]"
./build/core/pulsim_benchmarks "[ac_sweep][microbench]"

Reference CSV traces for the 10 converter showcases live under benchmarks/baselines/. A 1.0-native regression runner that consumes them is on the roadmap.

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Roadmap

Version	Highlight	Status
v1.3.0	In-house real sparse LU (KLU removed)	✅ shipped
v1.4.0	Complex sparse LU + multi-bit + parametric refactor + Python helpers	✅ shipped (this release)
v1.5.0	Reachability-based sparse triangular solve (close the n ≥ 64 Eigen gap on AC sweep)	planned
v1.6.0	AC-sweep symbolic-reuse: amortise analyze across all frequencies	planned
v1.7.0	BTF block-triangular ordering — composes with path-union for an extra ~2× on wide multi-bit transitions	planned
v2.0.0	TBD — first breaking Python API change since v1.0.0	TBD

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Citation

If you use Pulsim in academic work, please cite:

@software{pulsim,
  author  = {Gili, Luiz Carlos},
  title   = {Pulsim: A piecewise-linear state-space simulator for switched-mode power electronics},
  version = {1.4.0},
  url     = {https://github.com/lgili/Pulsim},
  year    = {2026},
}

Machine-readable CITATION.cff is in the repo root.

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Contributing

PRs welcome. Workflow:

1. Open an issue describing the change (or pick one from the issue tracker).
2. For non-trivial work, draft an OpenSpec proposal under openspec/changes/<name>/ (see existing proposals under openspec/changes/archive/ for the format).
3. Implement + add tests under core/tests/ (C++) or python/tests/ (Python).
4. Ensure all 498 C++ tests + 6 Python tests pass and openspec validate --strict is green.
5. Open the PR; CI runs on every push.

See CONTRIBUTING.md for the full guide.

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License

MIT © 2026 Luiz Carlos Gili. Free for academic + commercial use; please cite when used in academic work.

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Documentation deployment

Docs are published by .github/workflows/docs.yml using MkDocs Material + mike:

• PR: strict docs build (no deploy)
• main: deploys to the dev channel
• vX.Y.Z tag: deploys release docs and updates latest

In repository settings, set Pages Source to GitHub Actions.

Live docs: https://lgili.github.io/Pulsim/