trust-bo 0.1.0

CPU-only Trust Region Bayesian Optimization (TuRBO-style) with a Rust core, built for CFD-scale budgets

TRust-BO

Bayesian optimization that runs on the hardware you already have.

TRust-BO is not a CFD solver. It sits in the optimization loop — it does not replace the CFD solver; it reduces the number of CFD runs needed during design search.

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Why this exists

The best design should not require the best hardware.

CFD optimization has long been gated by HPC clusters, workstations, and institutional budgets — tools that are excellent, but out of reach for most people. TRust-BO was built by a high school student to solve a concrete problem: optimizing aerodynamics for a Formula Student car, on a laptop, without a GPU. The goal is to make Bayesian optimization fast enough to run anywhere, simple enough to use without a PhD, and accurate enough to matter in real engineering work.

TRust-BO is a Trust Region Bayesian Optimization engine written in Rust, exposed to Python via PyO3. No GPU required. No cloud required. pip install trust-bo and run locally.

What it does, precisely: on high-dimensional (≥50D) or noisy / constrained problems (such as real CFD), TRust-BO runs 5–10× faster than BoTorch TuRBO while reaching equal or better quality. On low-dimensional, smooth, small-budget problems a GP-based method (BoTorch, HEBO) is usually a better choice — TRust-BO does not claim to be universally best. See docs/PERFORMANCE_ASSESSMENT.md for the honest, data-backed breakdown.

Benchmark scope (read before comparing): results below are measured against BoTorch TuRBO, CMA-ES, Random Search, and NSGA-II. A comparison against SAASBO (a strong high-dimensional BO baseline) is future work. CFD benchmarks use 3 seeds and multi-objective uses 2 seeds; more seeds are planned for statistical rigor.

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How it compares

Feature	TRust-BO	BoTorch	HEBO	Optuna
GPU required	✗	optional	✗	✗
CPU-optimized core	✓ (Rust)	△	△	✓
High-dimensional (50D+)	✓	✓	△	△
Minimal ask/tell API	✓	△	△	✓
CFD workflow focus	✓	✗	✗	✗

BoTorch and Optuna can also be used for CFD-driven optimization. TRust-BO is specifically designed around a lightweight, CPU-first workflow for students and small engineering teams.

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Installation

pip install trust-bo

Prebuilt wheels (abi3, Python ≥3.9) are published for Linux, macOS, and Windows — no Rust toolchain needed for a normal install.

To build from source instead (requires a Rust toolchain):

git clone https://github.com/K092203/TRust-BO
cd TRust-BO
python -m venv .venv && source .venv/bin/activate
pip install .

For development, use maturin for fast rebuilds: pip install maturin && maturin develop --release.

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Usage

from trust_bo import TRustBOEngine, Float

# 1. Define the search space
space = [Float(f"x{i}", -5.0, 5.0) for i in range(10)]

# 2. Create the engine
engine = TRustBOEngine(space=space, direction="minimize", seed=42)

# 3. Ask → evaluate → tell
for _ in range(20):                          # 20 rounds × batch_size=10
    candidates = engine.ask(batch_size=10)   # suggest next points
    results = [
        {"value": your_cfd_solver(c), "feasible": True}
        for c in candidates
    ]
    engine.tell(candidates, results)         # feed results back

# 4. Get the best result
print(engine.best())
# {'parameters': {'x0': 0.12, ...}, 'objective_values': [3.47]}

With constraints

engine.tell(candidates, [
    {"value": solver(c), "feasible": constraint_ok(c)}
    for c in candidates
])

Multi-objective (Pareto)

Optimize several objectives at once via MultiObjectiveEngine. method="ehvi" uses a closed-form 2-objective Expected Hypervolume Improvement implemented in Rust; method="chebyshev" uses scalarization (any number of objectives).

from trust_bo import MultiObjectiveEngine, Float

space = [Float(f"x{i}", 0.0, 1.0) for i in range(20)]
engine = MultiObjectiveEngine(
    space=space, directions=["maximize", "minimize"],  # e.g. Cl ↑, Cd ↓
    method="ehvi", seed=0,
)
for _ in range(15):
    cands = engine.ask(batch_size=4)
    results = [{"values": [cl(c), cd(c)], "feasible": True} for c in cands]
    engine.tell(cands, results)

print(engine.pareto_front())                 # non-dominated designs
print(engine.hypervolume(ref=[0.0, 0.05]))   # quality metric

Save and resume

engine.save("study.zip")
engine = TRustBOEngine.load("study.zip")

Use as an Optuna sampler

Use TRust-BO as a drop-in Optuna sampler. Requires pip install optuna.

import optuna
from trust_bo.integrations.optuna import TrustBoOptunaSampler

study = optuna.create_study(direction="minimize", sampler=TrustBoOptunaSampler(seed=42))

def objective(trial):
    x = [trial.suggest_float(f"x{i}", -5.0, 5.0) for i in range(10)]
    return sum(v**2 for v in x)

study.optimize(objective, n_trials=100)
print(study.best_value)

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Benchmarks

Full data and methodology: docs/BENCHMARK.md and docs/PERFORMANCE_ASSESSMENT.md. Compared against BoTorch TuRBO, CMA-ES, Random Search, NSGA-II; SAASBO is future work.

Synthetic high-dimensional (the strong case)

Ackley / Rastrigin / Levy at 50D and 100D, budget 100–500, enable_phase2=True. Lower is better. Quality = median best over seeds; speed = wall-clock per run.

Condition	TRust-BO	BoTorch TuRBO	Quality	Speed
Ackley 50D, b=300	5.96	7.25	TRust	8.9×
Levy 50D, b=300	152.4	194.5	TRust	10.7×
Ackley 100D, b=300	7.13	8.51	TRust	5.7×
Levy 100D, b=300	284.1	723.2	TRust	5.7×
Ackley 50D, b=500	4.64	6.38	TRust	—

TRust-BO wins 16 of 18 mid-budget conditions and 5 of 5 at budget=500, while running 5–10× faster. The two losses are Rastrigin at budget=100 (small-budget GP edge).

Real CFD — airfoil shape optimization

H-1 (NeuralFoil, 16D CST, maximize Cl/Cd, 10 seeds): clean surrogate, well-posed.

Method	median Cl/Cd	best
BoTorch TuRBO	241.4	245.4
TRust-BO+P2	227.9	267.4
CMA-ES	223.3	265.5
Random	148.7	161.1

At 16D smooth, BoTorch leads on median; TRust-BO reaches the single best design.

H-2 (SU2 RANS, real Navier-Stokes, 16D, 3 seeds): noisy, mesh-constrained.

Method	median Cl/Cd	seeds in physical range
TRust-BO+P2	171.6	3 / 3
BoTorch TuRBO	126.1	3 / 3
CMA-ES	774.6 ⚠	1 / 3
Random	316.1 ⚠	1 / 3

On the harder, noisier RANS problem TRust-BO leads (+36%) and is the most stable.

⚠ Note on the SU2 (H-2) example: the feasibility check is currently simple (Cd > 0 + mesh validity). Ultra-thin shapes can slip through and produce non-physical Cl/Cd (the ⚠ values above). Geometric constraints (minimum thickness/area) are planned. For a clean, ready-to-use CFD example, prefer the NeuralFoil (H-1) pipeline.

Multi-objective (Cl ↑ and Cd ↓ simultaneously)

EHVI (closed-form 2-objective expected hypervolume improvement, in Rust) vs Chebyshev scalarization on SU2 (budget=60, 2 seeds): EHVI median hypervolume 0.0239 vs 0.0165 (+45%). Chebyshev produces a more diverse Pareto front. See docs/BENCHMARK.md.

Native Phase 2

A pure-Rust Matern 5/2 micro-GP fits the residuals of the MLP ensemble near the best point, refining the endgame. One flag, no sklearn, no extra deps:

engine = TRustBOEngine(space=space, config={"enable_phase2": True})

When to use TRust-BO: high-dimensional (50D+) or noisy/constrained problems, moderate-to-large budget (100–1000), and anywhere wall-clock per BO round matters — such as CFD where each evaluation already costs minutes to hours.

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How it works

TRust-BO implements TuRBO (Trust Region Bayesian Optimization) with a custom surrogate:

Cold start  →  Halton quasi-random sampling (n_init points)
Warm path   →  MLP Bootstrap Ensemble surrogate (5 members)
             + Cross-Entropy Method (CEM) within Trust Region
             + Trust Region dynamics (expand on success, shrink on failure)

Key design choices:

• Rust core — the inner loop (surrogate training, CEM, TR management) runs in compiled Rust via PyO3, keeping CPU usage low
• Warm start — surrogate weights are serialized between rounds, cutting training time by ~41%
• Neutral center stability — TR center only moves on ≥1% relative improvement, preventing instability from minor fluctuations
• No GPU — uses burn with the ndarray backend; a modern laptop CPU is sufficient

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Roadmap

Current (v0.1.x)

• Single Trust Region (exploitation-focused)
• MLP Bootstrap Ensemble surrogate with warm start
• Constraint handling (feasibility surrogate)
• 91-test suite (63 Python + 28 Rust), CPU-only, PyO3 Python bindings
• Benchmark vs BoTorch TuRBO / CMA-ES / HEBO / Random / NSGA-II
• Native Phase 2 (Rust Tandem Residual-GP): +32% at 50D, +55% at 10D, zero extra deps
• Async parallel / rolling evaluation (SLURM-ready) for expensive solvers
• Real CFD airfoil optimization — NeuralFoil (H-1) and SU2 RANS (H-2) pipelines
• Multi-objective: Chebyshev scalarization + closed-form 2-objective EHVI (Rust)
• Optuna sampler integration
• PyPI release (prebuilt abi3 wheels for Linux/macOS/Windows)

Planned

• SAASBO comparison + more benchmark seeds (statistical rigor)
• Geometric shape constraints for CFD (minimum thickness/area)
• Multi-objective beyond 2 objectives
• Multi-TR (TuRBO-M) — deprioritized; single TR is more stable at CFD-scale budgets
• Research write-up on lightweight BO for engineering design

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FAQ

Is TRust-BO a CFD solver?

No. TRust-BO is an optimizer that works with external CFD solvers. It does not solve Navier-Stokes equations. It decides which design candidates to evaluate next, reducing the total number of expensive CFD runs.

Why not just use BoTorch?

BoTorch is excellent and much more flexible. TRust-BO is not a replacement — it is a smaller CPU-first engine with a simple ask/tell API, designed around lightweight CFD-driven workflows for students and small teams.

Has it been validated on real CFD?

Yes, on airfoil shape optimization. Two pipelines are included: NeuralFoil (H-1, a fast learned aerodynamics surrogate) and SU2 RANS (H-2, real steady Navier-Stokes, Ma=0.3, Re=3×10⁶, SA turbulence). See the Benchmarks section and docs/BENCHMARK.md. Validation so far uses 3 seeds for SU2; more seeds and a SAASBO comparison are planned.

Who is this for?

Students, Formula Student teams, and small engineering teams interested in CFD-driven design optimization without HPC or GPUs.

What is the relationship to BoTorch / HEBO / Optuna?

These are all excellent optimizers. TRust-BO does not aim to replace them. It focuses on a specific gap: a CPU-only, lightweight optimizer packaged around CFD-driven engineering design workflows.

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Known limitations

• Surrogate accuracy vs GP: The MLP bootstrap ensemble trades uncertainty calibration for speed. On low-dimensional, smooth problems with small budgets, GP-based methods (BoTorch, HEBO) typically do better (confirmed on the 16D NeuralFoil benchmark). TRust-BO's advantage is at 50D+ or on noisy/constrained problems.
• Benchmark seeds: synthetic results use 10 seeds, but real CFD (SU2) uses 3 seeds and multi-objective uses 2 seeds — statistically thin. More seeds are planned.
• No SAASBO comparison yet: the strong high-dimensional BO baseline SAASBO has not been benchmarked against (environment constraints). Claims are limited to vs BoTorch TuRBO / CMA-ES / Random / NSGA-II.
• CFD feasibility is simplified: the SU2 (H-2) pipeline checks only Cd > 0 and mesh validity, so ultra-thin shapes can yield non-physical Cl/Cd. Geometric constraints (minimum thickness/area) are planned. The NeuralFoil (H-1) pipeline does not have this issue.
• Multi-objective is 2-objective for EHVI: the closed-form EHVI is 2-objective; use Chebyshev scalarization for 3+ objectives.
• Warm-start weight transfer: surrogate weights are serialized as hex strings (~1 MB/round). Functional but inefficient; a binary transfer mechanism is planned.
• Multi-TR (n_trs > 1) is experimental: implemented and tested, but deprioritized for CFD-scale budgets where single-TR is more stable.

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Development log

The full development history — design decisions, phase-by-phase experiments, and benchmark notes — is kept in docs/DEVELOPMENT.md (Japanese).

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Contributing

Contributions are welcome. This is a one-person project so far, and any help — bug reports, benchmark results on real problems, documentation, or code — is genuinely appreciated.

If you use TRust-BO for a CFD problem and get results (good or bad), please open an issue and share them. Real-world feedback is the most valuable thing at this stage.

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License

MIT © 2026 Kotaro Ozawa