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

• URL: http://arxiv.org/abs/2601.05098v2
• Date: Tue, 13 Jan 2026 05:17:15 GMT
• Title: ECLIPSE: An Evolutionary Computation Library for Instrumentation Prototyping in Scientific Engineering
• 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,
• Abstract summary: We present ECLIPSE, an evolutionary framework built to interface directly with complex, domain-specific simulation tools.<n>We demonstrate ECLIPSE across several active space-science applications, including evolved 3D antennas and spacecraft optimized for drag reduction in very low Earth orbit.
• Score: 1.6736150071247582
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. Evolutionary computation typically requires numerous design evaluations, making the integration of slow, low-throughput simulators particularly challenging, 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 simulator'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 drag reduction in very low Earth orbit. 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 designs for scientific hardware while leveraging existing domain-specific simulation software.

Related papers

• Grounding LLMs in Scientific Discovery via Embodied Actions [84.11877211907647]
  Large Language Models (LLMs) have shown significant potential in scientific discovery but struggle to bridge the gap between theoretical reasoning and physical simulation.<n>We propose EmbodiedAct, a framework that transforms established scientific software into active embodied agents by groundings in embodied actions with a tight perception-execution loop.
  arXiv  Detail & Related papers  (2026-02-24T07:37:18Z)
• Neural surrogates for designing gravitational wave detectors [21.601009915564344]
  We show how neural surrogate models can significantly reduce reliance on traditional, CPU-based simulators.<n>We train a neural network to surrogate the gravitational wave physics simulator Finesse, which was developed by the LIGO community.<n>Our algorithm loops between training the surrogate, inverse designing new experiments, and verifying their properties with the slow simulator for further training.
  arXiv  Detail & Related papers  (2025-11-24T17:58:59Z)
• Physics-Learning AI Datamodel (PLAID) datasets: a collection of physics simulations for machine learning [0.15469999759898032]
  PLAID is a framework for representing and sharing datasets of physics simulations.<n> PLAID defines a unified standard for describing simulation data.<n>We release six datasets under the PLAID standard, covering structural mechanics and computational fluid dynamics.
  arXiv  Detail & Related papers  (2025-05-05T18:59:17Z)
• Generative Models for Fast Simulation of Cherenkov Detectors at the Electron-Ion Collider [0.0]
  We present an open, standalone fast simulation tool for Detection of Internally Reflected Cherenkov Light (DIRC) detectors.<n>Our framework incorporates a suite of generative models tailored to accelerate particle identification (PID) tasks.<n>This flexibility supports the development and benchmarking of novel DL-driven PID methods.
  arXiv  Detail & Related papers  (2025-04-26T22:33:08Z)
• GausSim: Foreseeing Reality by Gaussian Simulator for Elastic Objects [55.02281855589641]
  GausSim is a novel neural network-based simulator designed to capture the dynamic behaviors of real-world elastic objects represented through Gaussian kernels.<n>We leverage continuum mechanics and treat each kernel as a Center of Mass System (CMS) that represents continuous piece of matter.<n>In addition, GausSim incorporates explicit physics constraints, such as mass and momentum conservation, ensuring interpretable results and robust, physically plausible simulations.
  arXiv  Detail & Related papers  (2024-12-23T18:58:17Z)
• A Realistic Simulation Framework for Analog/Digital Neuromorphic Architectures [73.65190161312555]
  ARCANA is a software spiking neural network simulator designed to account for the properties of mixed-signal neuromorphic circuits.<n>We show how the results obtained provide a reliable estimate of the behavior of the spiking neural network trained in software, once deployed in hardware.
  arXiv  Detail & Related papers  (2024-09-23T11:16:46Z)
• Meent: Differentiable Electromagnetic Simulator for Machine Learning [0.6902278820907753]
  Electromagnetic (EM) simulation plays a crucial role in analyzing and designing devices with sub-wavelength scale structures. Meent is an EM simulation software that employs rigorous coupled-wave analysis (RCWA) We present three applications of Meent: 1) generating a dataset for training neural operator, 2) serving as an environment for the reinforcement learning of nanophotonic device optimization, and 3) providing a solution for inverse problems with gradient-based gradients.
  arXiv  Detail & Related papers  (2024-06-11T10:00:06Z)
• Neural Operators for Accelerating Scientific Simulations and Design [85.89660065887956]
  An AI framework, known as Neural Operators, presents a principled framework for learning mappings between functions defined on continuous domains. Neural Operators can augment or even replace existing simulators in many applications, such as computational fluid dynamics, weather forecasting, and material modeling.
  arXiv  Detail & Related papers  (2023-09-27T00:12:07Z)
• ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation [45.201929285600606]
  We present ClimSim-Online, which includes an end-to-end workflow for developing hybrid ML-physics simulators. The dataset is global and spans ten years at a high sampling frequency. We provide a cross-platform, containerized pipeline to integrate ML models into operational climate simulators.
  arXiv  Detail & Related papers  (2023-06-14T21:26:31Z)
• QuaSiMo: A Composable Library to Program Hybrid Workflows for Quantum Simulation [48.341084094844746]
  We present a composable design scheme for the development of hybrid quantum/classical algorithms and for applications of quantum simulation. We implement our design scheme using the hardware-agnostic programming language QCOR into the QuaSiMo library.
  arXiv  Detail & Related papers  (2021-05-17T16:17:57Z)
• Composable Programming of Hybrid Workflows for Quantum Simulation [48.341084094844746]
  We present a composable design scheme for the development of hybrid quantum/classical algorithms and for applications of quantum simulation. We implement our design scheme using the hardware-agnostic programming language QCOR into the QuaSiMo library.
  arXiv  Detail & Related papers  (2021-01-20T14:20:14Z)
• Integrating Machine Learning with HPC-driven Simulations for Enhanced Student Learning [0.0]
  We develop a web application that supports both HPC-driven simulation and the ML surrogate methods to produce simulation outputs. The evaluation of the tool via in-classroom student feedback and surveys shows that the ML-enhanced tool provides a dynamic and responsive simulation environment.
  arXiv  Detail & Related papers  (2020-08-24T22:48:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.