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

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Authors: Julie Anne Rolla
Cited by: 0

Abstract

Designing scientific instrumentation often requires exploring large, highly constrained design spaces using computationally expensive physics simulations. These simulators pose substantial challenges for integrating evolutionary computation (EC) into scientific design workflows, as they are optimized for accuracy and ease of use rather than throughput. We present ECLIPSE, an evolutionary computation framework built to interface directly with complex, domain-specific simulation tools while supporting flexible geometric and parametric representations of scientific hardware. ECLIPSE provides a modular architecture consisting of (1) Individuals, which encode hardware designs using domain-aware, physically constrained representations; (2) Evaluators, which prepare simulation inputs, invoke external simulators, and translate the simulators's outputs into fitness measures; and (3) Evolvers, which implement EC algorithms suitable for high-cost, limited-throughput environments. We demonstrate the utility of ECLIPSE across several active space-science applications, including evolved 3D antennas and spacecraft geometries optimized for very low Earth orbit drag conditions. We further discuss the practical challenges encountered when coupling EC with scientific simulation workflows, including interoperability constraints, parallelization limits, and extreme evaluation costs, and outline ongoing efforts to combat these challenges. ECLIPSE enables interdisciplinary teams of physicists, engineers, and EC researchers to collaboratively explore unconventional hardware designs while preserving existing scientific software ecosystems.

Fields of Study & Subjects

Metaheuristic Optimization Algorithms Research Evolutionary Algorithms and Applications Advanced Multi-Objective Optimization Algorithms

Publication details

Journal / source
JPL Data
Publication date
1 January 2026
Document type
Dataset