الملخص
Inverse design of physical systems (molecules, devices, microstructures) often reduces to optimizing a high-dimensional structure against an expensive black-box simulator. Direct search is difficult because the space is non-Euclidean, feasibility is hard to encode, and each evaluation is expensive. We present Co-PiLOT, a latent optimization approach that maps candidates through a generative encoder-decoder, uses the decoder as a learned validity prior, and searches the latent space with physics-informed black-box optimization. The framework is applied on the inverse design of magnesium alloy microstructure/texture. We develop a vision transformer based-encoder; paired with latent diffusion, diffusion transformer and rectified-flow transformer-based decoders on $\sim80{,}000$ EBSD-derived microstructure dataset to learn a minimal bottleneck, $z$. The ViT-FMDiT model ($z$=$768$) reconstructs high-fidelity microstructure images (FID $27.86$, MS-SSIM $0.178$), which our self-segmenting orientation codec converts into input grids for crystal plasticity solver. Finally, we introduce MERIDIAN, an active latent optimizer driven by deep-kernel Gaussian-process uncertainty, failure-aware feasibility prediction, manifold-aware trust regions, and target-aware acquisition. Within a budget of $160$ simulations, the ViT-FMDiT and MERIDIAN combination yields the best target-driven objective score, reducing the relative target error by $3$--$22\%$ against seven baselines (DANTE, TuRBO, BAxUS, CMA-ES, DDOM, SEIKO, DDPO) on the same decoder.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Guru, M. K., Nagar, M., vyas, A., Bohlen, J., Aydin, R., & Ben Khalifa, N. (2026). Co-PiLOT: Constrained Physics-Informed Latent Optimization for Target-Driven Inverse Design. https://omanscience.com/ar/articles/co-pilot-constrained-physics-informed-latent-optimization-for-target-driven-inverse-design
MLA 9
Guru, Mahish K., et al. "Co-PiLOT: Constrained Physics-Informed Latent Optimization for Target-Driven Inverse Design." https://omanscience.com/ar/articles/co-pilot-constrained-physics-informed-latent-optimization-for-target-driven-inverse-design.
شيكاغو (المؤلف–التاريخ)
Guru, Mahish K., Mayank Nagar, Ayush vyas, Jan Bohlen, Roland Aydin, and Noomane Ben Khalifa. 2026. "Co-PiLOT: Constrained Physics-Informed Latent Optimization for Target-Driven Inverse Design." https://omanscience.com/ar/articles/co-pilot-constrained-physics-informed-latent-optimization-for-target-driven-inverse-design.
هارفارد
Guru, M. K., Nagar, M., vyas, A., Bohlen, J., Aydin, R. and Ben Khalifa, N. (2026) 'Co-PiLOT: Constrained Physics-Informed Latent Optimization for Target-Driven Inverse Design', Available at: https://omanscience.com/ar/articles/co-pilot-constrained-physics-informed-latent-optimization-for-target-driven-inverse-design.
فانكوفر
Guru MK, Nagar M, vyas A, Bohlen J, Aydin R, Ben Khalifa N. Co-PiLOT: Constrained Physics-Informed Latent Optimization for Target-Driven Inverse Design. https://omanscience.com/ar/articles/co-pilot-constrained-physics-informed-latent-optimization-for-target-driven-inverse-design
IEEE
M. K. Guru, M. Nagar, A. vyas, J. Bohlen, R. Aydin, and N. Ben Khalifa, "Co-PiLOT: Constrained Physics-Informed Latent Optimization for Target-Driven Inverse Design," https://omanscience.com/ar/articles/co-pilot-constrained-physics-informed-latent-optimization-for-target-driven-inverse-design.