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<p>To meet rapidly growing demands for multifunctional smart materials with dynamic energy-responsive capabilities, a unified modeling framework must integrate material design, structural optimization, and real-time adaptability. Existing methodologies tend to be disparate, thus separating topology optimization, inverse design, and structural analysis. A result of this fragmentation is inefficiency, nonoptimal performance under dynamic conditions, and limited adaptiveness to real-world uncertainties. This research proposes the Unified Deep Hybrid Analytical Framework for Smart Material Design and Energy-Responsive Structural Optimization (UDHF-SMERO) to fill these gaps. In under five tightly coupled stages of processing, each stage will focus on an engineered aspect of the design-to-deployment pipeline. The first region is Co-Evolutionary Physics Informed Graph Transformers (Co-PIGT), which model the concurrent co-learning of the patterning material cores and energy-dissipation pathways while guided by the physics Informed loss functions. The output is optimally attained graphs and generated spatio-temporal energy paths with R<sup>2</sup>&gt; 0.95 and dissipation error &lt; 3%. The next In line engages the Inverse Design GAN with Physics-Regularized Agents (IDG-PRA), which generates physically realizable microstructures and thermomechanical regimes using GAN-based sampling constrained by stress and thermal fields achieving inverse accuracy &gt; 92% and compliance error &lt; 5%. The third stage offers the Adaptive Dual-Regime Structural Transformer (ADReST) to capture static and dynamic load responses, resulting in natural frequency prediction errors &lt; 2.5%. The fourth module, Quantum Elastic Potential Mapper with Latent Annealing (QEPM-LA), develops an elastic meta-stability and probabilistic failure zone model encoded using quantum Inspired encodings, achieving &gt; 88% failure localization accuracy sets. Finally, the Contextual Deep Reinforcement Design Integrator (CDRDI) enables real-time policy updates from sensor feedback, achieving convergence in under 100 episodes and maintaining ≥ 95% adaptive performance. In the end, this holistic pipeline will link the design to prediction and adaptation, giving rise to the creation of next-generation smart structures, which boast superior resilience, tunability, and efficiency, irrespective of uncertain operational regimes. </p>

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UDHF-SMERO: a unified deep hybrid framework for co-evolutionary smart material design and energy-adaptive structural optimizations

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月23日

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  • Rajesh M. Bhagat,
  • Mangesh P. Bhorkar,
  • Rajesh M. Dhoble,
  • Buddharatna J. Godboley,
  • Amar Jain,
  • Hemant V. Hajare,
  • Prashant B. Pande

摘要

To meet rapidly growing demands for multifunctional smart materials with dynamic energy-responsive capabilities, a unified modeling framework must integrate material design, structural optimization, and real-time adaptability. Existing methodologies tend to be disparate, thus separating topology optimization, inverse design, and structural analysis. A result of this fragmentation is inefficiency, nonoptimal performance under dynamic conditions, and limited adaptiveness to real-world uncertainties. This research proposes the Unified Deep Hybrid Analytical Framework for Smart Material Design and Energy-Responsive Structural Optimization (UDHF-SMERO) to fill these gaps. In under five tightly coupled stages of processing, each stage will focus on an engineered aspect of the design-to-deployment pipeline. The first region is Co-Evolutionary Physics Informed Graph Transformers (Co-PIGT), which model the concurrent co-learning of the patterning material cores and energy-dissipation pathways while guided by the physics Informed loss functions. The output is optimally attained graphs and generated spatio-temporal energy paths with R2> 0.95 and dissipation error < 3%. The next In line engages the Inverse Design GAN with Physics-Regularized Agents (IDG-PRA), which generates physically realizable microstructures and thermomechanical regimes using GAN-based sampling constrained by stress and thermal fields achieving inverse accuracy > 92% and compliance error < 5%. The third stage offers the Adaptive Dual-Regime Structural Transformer (ADReST) to capture static and dynamic load responses, resulting in natural frequency prediction errors < 2.5%. The fourth module, Quantum Elastic Potential Mapper with Latent Annealing (QEPM-LA), develops an elastic meta-stability and probabilistic failure zone model encoded using quantum Inspired encodings, achieving > 88% failure localization accuracy sets. Finally, the Contextual Deep Reinforcement Design Integrator (CDRDI) enables real-time policy updates from sensor feedback, achieving convergence in under 100 episodes and maintaining ≥ 95% adaptive performance. In the end, this holistic pipeline will link the design to prediction and adaptation, giving rise to the creation of next-generation smart structures, which boast superior resilience, tunability, and efficiency, irrespective of uncertain operational regimes.