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Results for “algorithms optimization”
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Genetic algorithm optimization of defect clusters in crystalline materials
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Inverse design of hypoeutectoid pearlite steel microstructures using a deep learning and genetic algorithm optimization framework
Goal-oriented microstructure design in metallic materials is a challenging task due to complex structure-property relationships. Traditional experimental and computational approaches are time-intensive and economically inefficient, limiting their applicability for large-scale design space exploration. Here, in this work, we propose an end-to-end framework that integrates deep learning models with genetic optimization to design microstructures with targeted mechanical properties. Deep learning models enable accurate forward design, while their integration with genetic optimization enables efficient inverse design within a few hours, compared to days or weeks using conventional finite element simulations. The framework combines experimental characterization and finite element modeling to analyze the influence of microstructural features on the mechanical behavior of hypoeutectoid steels. Data from both experiments and simulations are used to train the deep learning models. To demonstrate its effectiveness, we apply the framework to 0.63% carbon steel with proeutectoid ferrite and pearlite phases, commonly used in industrial applications. In this study, 2D microstructures were used for modeling, selected primarily for computational efficiency and to establish proof of concept. The framework successfully optimizes microstructures for targeted yield strength, ultimate strength, and stress concentration factors while significantly reducing computational time. Beyond hypoeutectoid steels, this scalable framework can be extended to other material systems and integrated with additive manufacturing, offering an efficient approach for accelerating microstructure design for specific engineering applications.
Multimaterial layout optimization of truss structures via an improved particle swarm optimization algorithm
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Optimization algorithm for the generation of ONCV pseudopotentials
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Adaptive real-time congestion management in smart power systems using a real-time hybrid optimization algorithm
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Inverse transport calculations in optical imaging with subspace optimization algorithms
Inverse boundary value problems for the radiative transport equation play an important role in optics-based medical imaging techniques such as diffuse optical tomography (DOT) and fluorescence optical tomography (FOT). Despite the rapid progress in the mathematical theory and numerical computation of these inverse problems in recent years, developing robust and efficient reconstruction algorithms remains a challenging task and an active research topic. We propose here a robust reconstruction method that is based on subspace minimization techniques. The method splits the unknown transport solution (or a functional of it) into low-frequency and high-frequency components, and uses singular value decomposition to analytically recover part of low-frequency information. Minimization is then applied to recover part of the high-frequency components of the unknowns. We present some numerical simulations with synthetic data to demonstrate the performance of the proposed algorithm.
Prediction of and genetic algorithm optimization on data induced uncertainty reduction with the use of an integral experiment
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Communication Lower Bounds and Optimal Algorithms for Numerical Linear Algebra.
Abstract not provided.
Inverse Design of Tetracene Polymorphs with Enhanced Singlet Fission Performance by Property-Based Genetic Algorithm Optimization
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Gnowee: A Hybrid Metaheuristic Optimization Algorithm for Constrained, Black Box, Combinatorial Mixed-Integer Design
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Fast optimization algorithms and the cosmological constant
Denef and Douglas have observed that in certain landscape models the problem of finding small values of the cosmological constant is a large instance of a problem that is hard for the complexity class NP (Nondeterministic Polynomial-time). The number of elementary operations (quantum gates) needed to solve this problem by brute force search exceeds the estimated computational capacity of the observable Universe. Here in this paper, we describe a way out of this puzzling circumstance: despite being NP-hard, the problem of finding a small cosmological constant can be attacked by more sophisticated algorithms whose performance vastly exceeds brute force search. In fact, in some parameter regimes the average-case complexity is polynomial. We demonstrate this by explicitly finding a cosmological constant of order 10 -120 in a randomly generated 10 9 -dimensional Arkani-Hamed–Dimopoulos–Kachru landscape.
Adaptive quantum approximate optimization algorithm for solving combinatorial problems on a quantum computer
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Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices
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Quantum error mitigations for quantum approximate optimization algorithms on IBM quantum processors
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Application of a Novel Saddle Point Optimization Algorithm on Surface Reactions Involving Bidentate Adsorbates.
Abstract not provided.