Generative prediction of laser-induced rocket ignition with dynamic latent space representations

2026-03-08

AI Summary

This work addresses the prohibitive computational cost of high-fidelity simulations for laser-ignited rocket engines, which arise from the complex interplay of turbulent mixing, laser energy deposition, and high-speed flame propagation, thereby hindering efficient parametric exploration. To overcome this challenge, the study introduces a novel data-driven surrogate model that uniquely integrates convolutional autoencoders (cAE) with Neural Ordinary Differential Equations (Neural ODEs). This framework learns and predicts the full spatiotemporal evolution of the ignition process within a low-dimensional, dynamically evolving latent space. The proposed approach achieves speedups of several orders of magnitude while preserving physically interpretable flowfield outputs, enabling real-time digital twins, rapid parameter sweeps, and uncertainty quantification.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Deep Generative Models & Autoencoders
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Accurate and predictive scale-resolving simulations of laser-ignited rocket engines are highly time-consuming because the problem includes turbulent fuel-oxidizer mixing dynamics, laser-induced energy deposition, and high-speed flame growth. This is conflated with the large design space primarily corresponding to the laser operating conditions and target location. To enable rapid exploration and uncertainty quantification, we propose a data-driven surrogate modeling approach that combines convolutional autoencoders (cAEs) with neural ordinary differential equations (neural ODEs). The present target application of an ML-based surrogate model to leading-edge multi-physics turbulence simulation is part of a paradigm shift in the deployment of surrogate models towards increasing real-world complexity. Sequentially, the cAE spatially compresses high-dimensional flow fields into a low-dimensional latent space, wherein the system's temporal dynamics are learned via neural ODEs. Once trained, the model generates fast spatiotemporal predictions from initial conditions and specified operating inputs. By learning a surrogate to replace the entirety of the time-evolving simulation, the cost of predicting an ignition trial is reduced by several orders of magnitude, allowing efficient exploration of the input parameter space. Further, as the current framework yields a spatiotemporal field prediction, appraisal of the model output's physical grounding is more tractable. This approach marks a significant step toward real-time digital twins for laser-ignited rocket combustors and represents surrogate modeling in a complex system context.

Problem

• laser-induced ignition
• rocket combustion
• scale-resolving simulation
• turbulent mixing
• design space exploration

Innovation

• convolutional autoencoder
• neural ODE
• surrogate modeling
• laser ignition
• digital twin