108 papers indexed here
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This paper introduces an innovative framework for analyzing and optimizing fractal networks, leveraging the concept of 'Symmetry Metric' to guide network design. We propose a novel method for quantifying network structure using a mathematical definition of self-similarity and employing this metric to optimize network p...
Temporal Topology Optimization (TTO) presents a novel approach to biological system optimization, moving beyond static parameterization to model the dynamic evolution of topological states. This paper introduces a system leveraging network theory and evolutionary dynamics to predict emergent behaviors within biological...
This paper investigates the algorithmic complexity of parallel simulations of complex systems, aiming to develop a novel algorithm that significantly reduces computational costs while maintaining accuracy. Complex systems, such as protein folding and climate modeling, demand substantial computational resources. Existin...
Protein folding is a fundamental biological process, and achieving accurate and stable folding is critical for protein function. Current methods often struggle to preserve the intricate network of interactions within the protein structure, leading to misfolded proteins and disease. This paper introduces a novel Dynamic...
This paper investigates the algorithmic complexity of parallel simulations of complex systems, aiming to develop a novel algorithm that significantly reduces computational costs while maintaining accuracy. Complex systems, such as protein folding and climate modeling, demand substantial computational resources. Existin...
Temporal Topology Optimization (TTO) presents a novel approach to biological system optimization, moving beyond static parameterization to model the dynamic evolution of topological states. This paper introduces a system leveraging network theory and evolutionary dynamics to predict emergent behaviors within biological...
This paper introduces a novel self-adaptive optimization algorithm designed for modeling biological molecules. Biological systems exhibit intricate and dynamic behaviors, making traditional optimization methods often inadequate. This algorithm addresses this limitation by incorporating a self-adjustment mechanism that...
Protein folding is a fundamental biological process, and achieving accurate and stable folding is critical for protein function. Current methods often struggle to preserve the intricate network of interactions within the protein structure, leading to misfolded proteins and disease. This paper introduces a novel Dynamic...
This paper introduces a novel framework for quantum computing that integrates adaptive quantum state generation algorithms with the self-adaptive geometry of topology. The core idea is to combine the precision of quantum computation with the inherent flexibility of topological structures. We propose a method for dynami...
This paper introduces an innovative framework for analyzing and optimizing fractal networks, leveraging the concept of 'Symmetry Metric' to guide network design. We propose a novel method for quantifying network structure using a mathematical definition of self-similarity and employing this metric to optimize network p...
Quantum-driven constraint on complex systems aims to accelerate solution processes by dynamically adjusting constraint parameters. This paper explores the potential of quantum annealing and variational quantum eigensolver (VQE) to optimize these parameters. The core claim is to implement an algorithm that dynamically a...
This paper introduces a novel self-adaptive optimization algorithm designed for modeling biological molecules. Biological systems exhibit intricate and dynamic behaviors, making traditional optimization methods often inadequate. This algorithm addresses this limitation by incorporating a self-adjustment mechanism that...
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