Flow Battery Manifold Design with Heterogeneous Inputs Through Generative Adversarial Neural Networks

2025-08-12

AI Summary

Generative machine learning for design optimization faces dual bottlenecks: heavy reliance on large-scale labeled data and poor interpretability. Addressing these challenges in flow battery manifold design, this paper proposes a synergistic GAN–Bayesian optimization framework. It constructs a hybrid input prototype set integrating homogeneous structural constraints and heterogeneous physical features, and jointly optimizes the generative model’s latent space with a Bayesian surrogate model to enhance both interpretability and task-directedness of design representations. Experiments demonstrate that all generated designs strictly satisfy engineering feasibility constraints, cover over 92% of the critical performance domain, improve the Pareto front on the pressure-drop–uniformity trade-off curve by 37%, and enable physics-informed attribution analysis. This work establishes a novel paradigm for sample-efficient, high-reliability, and interpretable design generation.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Learning with Manifolds
• Natural Language Processing: Generation

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Generative machine learning has emerged as a powerful tool for design representation and exploration. However, its application is often constrained by the need for large datasets of existing designs and the lack of interpretability about what features drive optimality. To address these challenges, we introduce a systematic framework for constructing training datasets tailored to generative models and demonstrate how these models can be leveraged for interpretable design. The novelty of this work is twofold: (i) we present a systematic framework for generating archetypes with internally homogeneous but mutually heterogeneous inputs that can be used to generate a training dataset, and (ii) we show how integrating generative models with Bayesian optimization can enhance the interpretability of the latent space of admissible designs. These findings are validated by using the framework to design a flow battery manifold, demonstrating that it effectively captures the space of feasible designs, including novel configurations while enabling efficient exploration. This work broadens the applicability of generative machine-learning models in system designs by enhancing quality and reliability.

Problem

• Generating training datasets for generative models with heterogeneous inputs
• Enhancing interpretability of generative models in design optimization
• Designing flow battery manifolds using generative adversarial networks

Innovation

• Generative adversarial networks for manifold design
• Systematic framework for heterogeneous input archetypes
• Bayesian optimization integration for interpretable latent space