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Deep learning-based cloth simulation approaches have potential in achieving real-time simulation of complex cloth by directly learning a mapping from control input to resulting cloth movement, bypassing the need for time-consuming dynamic solving and ...

While network simulation is widely used for evaluating network protocols and applications, ensuring realism remains a key challenge. There has been much work on simulating network mechanisms faithfully (e.g., links, buffers, etc.), but less attention on ...

While network simulation is widely used for evaluating network protocols and applications, ensuring realism remains a key challenge. There has been much work on simulating network mechanisms faithfully (e.g., links, buffers, etc.), but less attention on ...

In recent years, the performance of neural network inference has been drastically improved. This rapid change has paved the way for research projects focusing on accelerating physics-based simulations by replacing solver with its approximation. In this ...

We present a vision of data-informed network simulation to address significant shortcomings in the state of the art. We substantiate our position with proof points based on iBox, which leverages networking domain knowledge and machine learning (ML) ...

In this paper, we propose a molecular communication system where bio-nanomachines cooperate in signaling and move directionally toward a target area in the environment. In the proposed system, bio-nanomachines, upon entering a target area, release ...

In physically-based simulation, it is essential to choose appropriate material parameters to generate desirable simulation results. In many cases, however, choosing appropriate material parameters is very challenging, and often tedious trial-and-error ...

We study the effects of rescheduling on no-show behavior in an outpatient appointment system for both new and follow-up patients. Previous literature has primarily focused on new patients and investigated the role of waiting time on no-show probability. ...

Epidemic information dissemination is a promising technique for delivering messages in sparse mobile networks. This paper considers epidemic information dissemination in mobile molecular communication and conducts data-driven simulations. In the data-...

Data-driven methods for physical simulation are an attractive option for interactive applications due to their ability to trade precomputation and memory footprint in exchange for improved runtime performance. Yet, existing data-driven methods fall ...

Artificial life (ALife) examines systems related to natural life, its processes, and its evolution, using simulations with computer models, robotics, and biochemistry. In this article, we focus on the computer modeling, or “soft,” aspects of ALife and ...

Computer simulations are widely used in various fields of science and engineering, including computational fluid dynamics. As the demand for the accuracy and quality of simulations grows, the cost of executing simulations itself is also rapidly ...

Crafting the behavior of a deformable object is difficult---whether it is a biomechanically accurate character model or a new multimaterial 3D printable design. Getting it right requires constant iteration, performed either manually or driven by an ...

The combination of social network extraction from texts, network analytics to identify key actors, and then simulation to assess alternative interventions in terms of their impact on the network is a powerful approach for supporting crisis de-escalation ...