Zobrazeno 1 - 10
of 1 612
pro vyhledávání: '"Lusch A"'
Autor:
Barwey, Shivam, Balin, Riccardo, Lusch, Bethany, Patel, Saumil, Balakrishnan, Ramesh, Pal, Pinaki, Maulik, Romit, Vishwanath, Venkatram
This work develops a distributed graph neural network (GNN) methodology for mesh-based modeling applications using a consistent neural message passing layer. As the name implies, the focus is on enabling scalable operations that satisfy physical cons
Externí odkaz:
http://arxiv.org/abs/2410.01657
Autor:
Barwey, Shivam, Pal, Pinaki, Patel, Saumil, Balin, Riccardo, Lusch, Bethany, Vishwanath, Venkatram, Maulik, Romit, Balakrishnan, Ramesh
A graph neural network (GNN) approach is introduced in this work which enables mesh-based three-dimensional super-resolution of fluid flows. In this framework, the GNN is designed to operate not on the full mesh-based field at once, but on localized
Externí odkaz:
http://arxiv.org/abs/2409.07769
This work focuses on developing a data-driven framework using Koopman operator theory for system identification and linearization of nonlinear systems for control. Our proposed method presents a deep learning framework with recursive learning. The re
Externí odkaz:
http://arxiv.org/abs/2309.04074
Autor:
Shilpika, Lusch, Bethany, Emani, Murali, Simini, Filippo, Vishwanath, Venkatram, Papka, Michael E., Ma, Kwan-Liu
The ability to monitor and interpret of hardware system events and behaviors are crucial to improving the robustness and reliability of these systems, especially in a supercomputing facility. The growing complexity and scale of these systems demand a
Externí odkaz:
http://arxiv.org/abs/2306.09457
Autor:
Yue Che, Carolin Wöltjen, Achim Lusch, Christian Winter, Stephan Trainer, Moritz Schirren, Stefan Sponholz, Wolfram Trudo Knoefel, Peter Albers, Andreas Hiester
Publikováno v:
World Journal of Surgical Oncology, Vol 22, Iss 1, Pp 1-6 (2024)
Abstract Introduction and objectives Postchemotherapy residual tumor resection (PC-RTR) is an important part of the multimodal treatment for patients with metastatic germ cell tumors. Simultaneous retroperitoneal and thoracic metastases often require
Externí odkaz:
https://doaj.org/article/46efd66a56b4499cb6286f9e7c67a7c6
Autor:
Maulik, Romit, Rao, Vishwas, Wang, Jiali, Mengaldo, Gianmarco, Constantinescu, Emil, Lusch, Bethany, Balaprakash, Prasanna, Foster, Ian, Kotamarthi, Rao
Data assimilation (DA) in the geophysical sciences remains the cornerstone of robust forecasts from numerical models. Indeed, DA plays a crucial role in the quality of numerical weather prediction, and is a crucial building block that has allowed dra
Externí odkaz:
http://arxiv.org/abs/2112.07856
Publikováno v:
Contemporary Accounting Research; Jun2024, Vol. 41 Issue 2, p719-747, 29p
Autor:
Egele, Romain, Maulik, Romit, Raghavan, Krishnan, Lusch, Bethany, Guyon, Isabelle, Balaprakash, Prasanna
Deep neural networks are powerful predictors for a variety of tasks. However, they do not capture uncertainty directly. Using neural network ensembles to quantify uncertainty is competitive with approaches based on Bayesian neural networks while bene
Externí odkaz:
http://arxiv.org/abs/2110.13511
We outline the development of a general-purpose Python-based data analysis tool for OpenFOAM. Our implementation relies on the construction of OpenFOAM applications that have bindings to data analysis libraries in Python. Double precision data in Ope
Externí odkaz:
http://arxiv.org/abs/2103.09389
We outline the development of a data science module within OpenFOAM which allows for the in-situ deployment of trained deep learning architectures for general-purpose predictive tasks. This module is constructed with the TensorFlow C API and is integ
Externí odkaz:
http://arxiv.org/abs/2012.00900