Physics-Informed Uncertainty Enables Reliable AI-driven Design

• URL: http://arxiv.org/abs/2601.18638v1
• Date: Mon, 26 Jan 2026 16:10:59 GMT
• Title: Physics-Informed Uncertainty Enables Reliable AI-driven Design
• Authors: Tingkai Xue, Chin Chun Ooi, Yang Jiang, Luu Trung Pham Duong, Pao-Hsiung Chiu, Weijiang Zhao, Nagarajan Raghavan, My Ha Dao,
• Abstract summary: Inverse design is a central goal in science and engineering, including frequency-selective surfaces that are critical to microelectronics for telecommunications and optical metamaterials.<n>Traditional surrogate-assisted optimization methods using deep learning can accelerate the design process but do not usually incorporate uncertainty quantification.<n>Here, we introduce and validate a fundamentally different paradigm of Physics-Informed Uncertainty, where the degree to which a model's prediction violates fundamental physical laws serves as a computationally-cheap and effective proxy for predictive uncertainty.
• Score: 1.1104649308580707
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Inverse design is a central goal in much of science and engineering, including frequency-selective surfaces (FSS) that are critical to microelectronics for telecommunications and optical metamaterials. Traditional surrogate-assisted optimization methods using deep learning can accelerate the design process but do not usually incorporate uncertainty quantification, leading to poorer optimization performance due to erroneous predictions in data-sparse regions. Here, we introduce and validate a fundamentally different paradigm of Physics-Informed Uncertainty, where the degree to which a model's prediction violates fundamental physical laws serves as a computationally-cheap and effective proxy for predictive uncertainty. By integrating physics-informed uncertainty into a multi-fidelity uncertainty-aware optimization workflow to design complex frequency-selective surfaces within the 20 - 30 GHz range, we increase the success rate of finding performant solutions from less than 10% to over 50%, while simultaneously reducing computational cost by an order of magnitude compared to the sole use of a high-fidelity solver. These results highlight the necessity of incorporating uncertainty quantification in machine-learning-driven inverse design for high-dimensional problems, and establish physics-informed uncertainty as a viable alternative to quantifying uncertainty in surrogate models for physical systems, thereby setting the stage for autonomous scientific discovery systems that can efficiently and robustly explore and evaluate candidate designs.

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