ECLIPSE: An Evolutionary Computation Library for Instrumentation Prototyping in Scientific Engineering
A 21-author team built a library that evolves hardware designs using existing physics simulators.
A large interdisciplinary team of 21 researchers has introduced ECLIPSE, a specialized evolutionary computation (EC) framework designed to tackle a major bottleneck in scientific engineering: designing complex hardware like space instruments. Traditional physics simulators are built for accuracy, not speed, making them poorly suited for EC algorithms that require thousands of evaluations. ECLIPSE solves this by providing a modular architecture that interfaces directly with these existing, slow simulators. Its three core components—Individuals (for domain-aware design encoding), Evaluators (for running simulations and scoring results), and Evolvers (for implementing EC algorithms)—allow teams to leverage their trusted simulation tools within an automated optimization loop.
The team demonstrated ECLIPSE's power on two novel space-science applications. First, they evolved 3D antenna designs for directional sensitivity, achieving performance roughly comparable to much more expensive two-antenna interferometric arrays. This represents a potential breakthrough in cost reduction for certain sensing applications. Second, they optimized spacecraft geometries for aerodynamic drag reduction, a critical factor for satellite longevity and fuel efficiency. By enabling this collaborative exploration between physicists, engineers, and EC experts, ECLIPSE opens the door to discovering non-intuitive, high-performance hardware designs that might be missed by traditional manual approaches, all while working within the constraints of real-world, high-fidelity simulation environments.
- ECLIPSE provides a modular framework (Individuals, Evaluators, Evolvers) to integrate evolutionary algorithms with slow, domain-specific physics simulators.
- It successfully evolved 3D antenna designs with directional sensitivity comparable to two-antenna interferometric arrays, indicating major potential cost savings.
- The framework also optimized spacecraft geometries for drag reduction, demonstrating its utility for real-world aerospace engineering challenges.
Why It Matters
This enables automated, AI-driven discovery of optimal scientific hardware, potentially slashing R&D costs and time for satellites and instruments.