159 papers indexed here
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This paper presents a novel phase-field modeling approach for fluid dynamics, specifically designed to accommodate and accurately represent non-Newtonian viscosity. Traditional phase-field methods often struggle to capture the complex behavior of fluids exhibiting non-linear viscosity distributions. We introduce a modi...
This paper explores the extension of standard diffusion equations to incorporate a landscape parameter, introducing a feedback loop that drives the evolution of diffusion patterns. Traditional diffusion models, while widely used, often lack the capacity to generate complex and unpredictable behavior. We propose a novel...
This research investigates the emergence of complex patterns within stochastic diffusion processes, proposing a novel approach centered on identifying and quantifying 'emergent complexity' through the application of a "statistical resonance" algorithm. We explore how this algorithm maps information flow across a networ...
This paper proposes a novel computational paradigm, termed Algorithmic Morphogenesis, which posits that complex computation can emerge through the controlled diffusion of algorithmic elements within a system. The core idea is that a collection of interconnected agents, each governed by a simple algorithmic rule, can, w...
Quantum annealing offers a novel approach to simulating diffusion processes, promising accelerated optimization and enhanced pattern generation. This paper explores the potential of this technique to address a critical bottleneck in simulating diffusion, specifically focusing on the efficient exploration of the solutio...
This paper presents a novel theoretical framework for understanding and modeling temporal dynamics, focusing on the propagation of patterns and information across spatial and temporal scales. The core of the work centers on the development of a model incorporating non-linear interactions and diffusion processes, utiliz...
Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains, including social network analysis, drug discovery, and recommendation systems. However, a significant limitation of many existing GNN models lies in their reliance on static attention mechanisms. These mechanisms often assign fixed we...
Accurate urban traffic flow prediction is crucial for intelligent transportation systems, urban planning, and resource allocation. Traditional traffic forecasting methods often struggle to capture the complex spatiotemporal dependencies inherent in urban traffic networks. This paper proposes a novel approach utilizing...
This paper explores the application of Graph Neural Networks (GNNs) for automated drug molecule design. Traditional drug discovery relies heavily on empirical methods and serendipitous findings, often suffering from high costs and low success rates. We propose a novel approach that leverages the inherent structural inf...
This paper investigates the application of Graph Neural Networks (GNNs) to the domain of hardware design space exploration. Traditional design exploration methods often struggle to effectively capture and optimize the intricate dependencies inherent in complex hardware architectures. We propose a novel framework utiliz...
Predicting protein function and understanding protein-protein interactions (PPIs) are central challenges in modern biology. Protein-protein interaction networks, representing these interactions, are inherently complex and dynamic. Traditional methods often struggle to capture the temporal evolution of these networks, f...
This paper presents a novel approach to robot control in complex, multi-agent environments by leveraging relational reinforcement learning (RRL) and graph neural networks (GNNs). The core idea is that robots benefit significantly from understanding and reasoning about the relationships between themselves and their surr...
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