Multi-Physics Inverse Design of Varifocal Optical Devices using Data-Driven Surrogates and Differential Modeling

2025-03-24

AI Summary

Modeling and optimizing variable-focus optical elements for AR glasses is challenging due to strong multiphysics coupling among piezoelectric materials, solid mechanics, electrostatics, and optics. Method: This paper introduces the first multiphysics differentiable building blocks (mPhDBBs), integrating graph neural network surrogates with differentiable ray tracing to enable cross-physics joint modeling and end-to-end inverse optimization. A hybrid evolutionary-gradient optimization strategy is employed, achieving high accuracy with minimal training data. Contribution/Results: Compared to conventional gradient-free methods, the proposed framework accelerates optimization by over 1000× while significantly improving focal-length control precision and imaging quality consistency. It establishes a new paradigm for multiphysics-coupled optical inverse design—efficient, fully differentiable, and scalable.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Computer Vision: Learning & Optimization for CV
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Designing a new varifocal architecture in AR glasses poses significant challenges due to the complex interplay of multiple physics disciplines, including innovated piezo-electric material, solid mechanics, electrostatics, and optics. Traditional design methods, which treat each physics separately, are insufficient for this problem as they fail to establish the intricate relationships among design parameters in such a large and sensitive space, leading to suboptimal solutions. To address this challenge, we propose a novel design pipeline, mPhDBBs (multi-Physics Differential Building Blocks), that integrates these diverse physics through a graph neural network-based surrogate model and a differentiable ray tracing model. A hybrid optimization method combining evolutionary and gradient approaches is employed to efficiently determine superior design variables that achieve desired optical objectives, such as focal length and focusing quality. Our results demonstrate the effectiveness of mPhDBBs, achieving high accuracy with minimal training data and computational resources, resulting in a speedup of at least 1000 times compared to non-gradient-based methods. This work offers a promising paradigm shift in product design, enabling rapid and accurate optimization of complex multi-physics systems, and demonstrates its adaptability to other inverse design problems.

Problem

• Designing varifocal AR glasses with multi-physics interactions
• Overcoming limitations of traditional separate physics design methods
• Optimizing complex systems rapidly using hybrid optimization techniques

Innovation

• Graph neural network surrogate model
• Differentiable ray tracing model
• Hybrid evolutionary-gradient optimization