Zobrazeno 1 - 10
of 617
pro vyhledávání: '"Zavala, Victor M"'
Soft gels, formed via the self-assembly of particulate organic materials, exhibit intricate multi-scale structures that provides them with flexibility and resilience when subjected to external stresses. This work combines molecular simulations and to
Externí odkaz:
http://arxiv.org/abs/2404.02991
Autor:
Cole, David L, Zavala, Victor M
Datasets encountered in scientific and engineering applications appear in complex formats (e.g., images, multivariate time series, molecules, video, text strings, networks). Graph theory provides a unifying framework to model such datasets and enable
Externí odkaz:
http://arxiv.org/abs/2401.11404
Autor:
Laky, Daniel J., Zavala, Victor M.
The Euler characteristic (EC) is a powerful topological descriptor that can be used to quantify the shape of data objects that are represented as fields/manifolds. Fast methods for computing the EC are required to enable processing of high-throughput
Externí odkaz:
http://arxiv.org/abs/2311.11740
Bayesian optimization (BO) has proven to be an effective paradigm for the global optimization of expensive-to-sample systems. One of the main advantages of BO is its use of Gaussian processes (GPs) to characterize model uncertainty which can be lever
Externí odkaz:
http://arxiv.org/abs/2311.11254
Industrial sectors such as urban centers, chemical companies, manufacturing facilities, and microgrids are actively exploring strategies to help reduce their carbon footprint. For instance, university campuses are complex urban districts (involving c
Externí odkaz:
http://arxiv.org/abs/2311.00809
Hierarchical optimization architectures are used in power systems to manage disturbances and phenomena that arise at multiple spatial and temporal scales. We present a graph modeling abstraction for representing such architectures and an implementati
Externí odkaz:
http://arxiv.org/abs/2309.10568
Graph Neural Networks (GNNs) have emerged as a prominent class of data-driven methods for molecular property prediction. However, a key limitation of typical GNN models is their inability to quantify uncertainties in the predictions. This capability
Externí odkaz:
http://arxiv.org/abs/2307.10438
Autor:
Zhang, Weiqi, Zavala, Victor M.
The power grid is undergoing significant restructuring driven by the adoption of wind/solar power and the incorporation of new flexible technologies that can shift load in space and time (e.g., data centers, battery storage, and modular manufacturing
Externí odkaz:
http://arxiv.org/abs/2303.10217
Anthropogenic pollution of hydrological systems affects diverse communities and ecosystems around the world. Data analytics and modeling tools play a key role in fighting this challenge, as they can help identify key sources as well as trace transpor
Externí odkaz:
http://arxiv.org/abs/2302.04991
The real-time operation of large-scale infrastructure networks requires scalable optimization capabilities. Decomposition schemes can help achieve scalability; classical decomposition approaches such as the alternating direction method of multipliers
Externí odkaz:
http://arxiv.org/abs/2212.11571