Zobrazeno 1 - 10
of 9 451
pro vyhledávání: '"Venkatasubramanian A"'
Electrolytes mediate interactions between the cathode and anode and determine performance characteristics of batteries. Mixtures of multiple solvents are often used in electrolytes to achieve desired properties, such as viscosity, dielectric constant
Externí odkaz:
http://arxiv.org/abs/2410.14689
Molecular Foundation Models are emerging as powerful tools for accelerating molecular design, material science, and cheminformatics, leveraging transformer architectures to speed up the discovery of new materials and drugs while reducing the computat
Externí odkaz:
http://arxiv.org/abs/2409.15370
In this work, we introduce a new approach to processing complex-valued data using DNNs consisting of parallel real-valued subnetworks with coupled outputs. Our proposed class of architectures, referred to as Steinmetz Neural Networks, leverages multi
Externí odkaz:
http://arxiv.org/abs/2409.10075
In many domains, it is difficult to obtain the race data that is required to estimate racial disparity. To address this problem, practitioners have adopted the use of proxy methods which predict race using non-protected covariates. However, these pro
Externí odkaz:
http://arxiv.org/abs/2409.01984
Safe, all-solid-state lithium metal batteries enable high energy density applications, but suffer from instabilities during operation that lead to rough interfaces between the metal and electrolyte and subsequently cause void formation and dendrite g
Externí odkaz:
http://arxiv.org/abs/2408.03175
As Artificial Intelligence (AI) tools are increasingly employed in diverse real-world applications, there has been significant interest in regulating these tools. To this end, several regulatory frameworks have been introduced by different countries
Externí odkaz:
http://arxiv.org/abs/2407.08689
In this work, we describe a novel approach to building a neural PDE solver leveraging recent advances in transformer based neural network architectures. Our model can provide solutions for different values of PDE parameters without any need for retra
Externí odkaz:
http://arxiv.org/abs/2407.06209
Vibrational properties of solids are key to determining stability, response and functionality. However, they are challenging to computationally predict at Ab-Initio accuracy, even for elemental systems. Ab-Initio methods for modeling atomic interacti
Externí odkaz:
http://arxiv.org/abs/2406.15491
Autor:
Venkatasubramanian, Shyam, Kang, Bosung, Pezeshki, Ali, Rangaswamy, Muralidhar, Tarokh, Vahid
This work presents a large-scale dataset for radar adaptive signal processing (RASP) applications, aimed at supporting the development of data-driven models within the radar community. The dataset, called RASPNet, consists of 100 realistic scenarios
Externí odkaz:
http://arxiv.org/abs/2406.09638
Autor:
Venkatasubramanian, Venkat
Large Language Models (LLMs) are often criticized for lacking true "understanding" and the ability to "reason" with their knowledge, being seen merely as autocomplete systems. We believe that this assessment might be missing a nuanced insight. We sug
Externí odkaz:
http://arxiv.org/abs/2406.06870