[Paper] ECLIPSE: An Evolutionary Computation Library for Instrumentation Prototyping in Scientific Engineering

Source: arXiv - 2601.05098v1

Overview

The paper introduces ECLIPSE, an open‑source evolutionary computation (EC) library designed to plug directly into heavyweight physics simulators used for scientific instrument design. By handling the heavy‑weight evaluation bottleneck, ECLIPSE lets engineers and researchers run EC‑driven optimization on real‑world, high‑fidelity models—something that was previously impractical for most development teams.

Key Contributions

• Domain‑aware representation layer: Encodes hardware (e.g., antennas, spacecraft structures) as “Individuals” that respect physical constraints and geometric semantics.
• Simulator‑centric evaluation pipeline: A plug‑and‑play “Evaluator” component automatically generates input files, launches external simulators, and maps raw simulation outputs to fitness scores.
• EC algorithms tuned for low‑throughput environments: Evolvers implement surrogate‑assisted, batch‑wise, and asynchronous strategies that reduce the number of expensive simulations needed.
• Modular, extensible architecture: Clear separation of Individuals, Evaluators, and Evolvers enables teams to swap in custom simulators or optimization heuristics with minimal code changes.
• Real‑world case studies: Demonstrated on 3‑D antenna design and low‑Earth‑orbit drag‑reduction geometry problems, showing measurable performance gains over manual design iterations.

Methodology

ECLIPSE follows a three‑tier design:

1. Individuals – Objects that store a candidate design using either parametric variables (e.g., length, curvature) or geometric primitives (meshes, CAD features). The library enforces domain constraints (material limits, symmetry, manufacturability) at creation time.

2. Evaluators – Middleware that translates an Individual into the exact input format required by a target physics simulator (e.g., CST, ANSYS, or custom CFD codes). After the simulator finishes, the Evaluator parses the results (S‑parameters, drag coefficients, etc.) and computes a scalar fitness value (or multi‑objective vector).

3. Evolvers – A suite of EC algorithms (genetic algorithms, CMA‑ES, surrogate‑assisted evolution) that are aware of the high cost of each evaluation. They employ techniques such as:

   • Batch evaluation – grouping candidates to exploit parallel simulator runs on HPC clusters.
   • Surrogate models – training cheap predictive models (Gaussian processes, neural nets) on previously evaluated designs to filter out low‑promise candidates before the expensive simulation step.
   • Asynchronous evolution – allowing new individuals to be injected as soon as any simulation finishes, keeping compute resources fully utilized.

The workflow is orchestrated via a lightweight configuration file, so a developer can spin up an optimization campaign with a few command‑line arguments.

Results & Findings

• 3‑D Antenna Optimization: Using ECLIPSE, the authors evolved antenna geometries that achieved a 12 % improvement in gain at the target frequency compared to the baseline design, after only ~200 high‑fidelity simulations (versus thousands required by a naïve grid search).
• Drag‑Reduction Geometry: For a low‑Earth‑orbit satellite bus, the framework discovered a shape that cut aerodynamic drag by 8 % while staying within strict volume and mass constraints, again with a modest simulation budget.
• Throughput Gains: By leveraging surrogate pre‑filtering, the number of full‑physics evaluations dropped by ~60 % without sacrificing final solution quality.
• Developer Experience: Teams reported a 30 % reduction in integration effort when swapping between different simulators, thanks to the standardized Evaluator API.

Practical Implications

• Accelerated Prototyping: Engineers can now run EC‑driven design loops on existing high‑fidelity tools without building custom optimization wrappers from scratch.
• Cost‑Effective Exploration: The surrogate‑assisted workflow makes it feasible to explore large, constrained design spaces on modest compute budgets (e.g., a small GPU cluster or cloud‑based HPC spot instances).
• Cross‑Disciplinary Collaboration: Physicists can focus on simulation fidelity while software developers handle the optimization orchestration, lowering the barrier for interdisciplinary projects.
• Reusable Components: The modular design means the same Evaluator can be reused across multiple projects (antenna, optics, thermal control), fostering code reuse and reducing technical debt.
• Open‑Source Ecosystem: As an openly available library, ECLIPSE can become a de‑facto standard for EC‑simulation coupling, encouraging community‑driven extensions (e.g., integration with JAX‑based differentiable simulators).

Limitations & Future Work

• Simulator Dependency: ECLIPSE assumes the external simulator can be invoked in a batch‑mode and that its I/O formats are stable; legacy tools with GUI‑only workflows still require manual wrappers.
• Scalability Ceiling: While surrogate models reduce evaluation counts, the overall wall‑clock time remains dominated by the longest‑running simulations; further work on distributed surrogate training and adaptive fidelity is needed.
• Multi‑Objective Trade‑offs: Current case studies focus on single‑objective fitness; extending the Evolvers to robustly handle Pareto‑front generation for competing metrics (e.g., mass vs. performance) is an active research direction.
• User‑Friendly UI: The present interface is configuration‑file driven; a visual dashboard for monitoring runs and tweaking parameters would broaden adoption among non‑programmer engineers.

ECLIPSE bridges the gap between cutting‑edge evolutionary algorithms and the heavyweight simulation tools that dominate scientific engineering, opening the door for faster, more innovative hardware design cycles.

Authors

• Max Foreback
• Evan Imata
• Vincent Ragusa
• Jacob Weiler
• Christina Shao
• Joey Wagner
• Katherine G. Skocelas
• Jonathan Sy
• Aman Hafez
• Wolfgang Banzhaf
• Amy Conolly
• Kyle R. Helson
• Rick Marcusen
• Charles Ofria
• Marcin Pilinski
• Rajiv Ramnath
• Bryan Reynolds
• Anselmo C. Pontes
• Emily Dolson
• Julie Rolla

Paper Information

• arXiv ID: 2601.05098v1
• Categories: cs.NE
• Published: January 8, 2026
• PDF: Download PDF