User profiles for Tianshu Bao
Tianshu BaoVanderbilt University Verified email at vanderbilt.edu Cited by 48 |
Partial differential equation driven dynamic graph networks for predicting stream water temperature
This paper presents a physics-guided machine learning approach that incorporates partial
differential equations (PDEs) in a graph neural network model to improve the prediction of …
differential equations (PDEs) in a graph neural network model to improve the prediction of …
Understanding and co-designing the data ingestion pipeline for industry-scale recsys training
…, B Gedik, S Pan, M Ozdal, R Komuravelli, J Pan, T Bao…�- CoRR, 2021 - openreview.net
Datacenter-scale AI training clusters consisting of thousands of domain-specific accelerators
(DSA) are used to train increasingly-complex deep learning models. These clusters rely on …
(DSA) are used to train increasingly-complex deep learning models. These clusters rely on …
[HTML][HTML] Effects of water saturation and loading rate on direct shear tests of andesite
T Bao, K Hashiba, K Fukui�- Journal of Rock Mechanics and Geotechnical�…, 2022 - Elsevier
For estimating the long-term stability of underground framework, it is vital to learn the mechanical
and rheological characteristics of rock in multiple water saturation conditions. However, …
and rheological characteristics of rock in multiple water saturation conditions. However, …
Effect of water saturation on the Brazilian tension test of rocks
T Bao, K Hashiba, K Fukui�- Materials Transactions, 2021 - jstage.jst.go.jp
Water accelerates the deformation and failure of rock and hence deteriorates the stability of
rock structures on and under the ground. However, most of the previous studies examined …
rock structures on and under the ground. However, most of the previous studies examined …
Physics guided neural networks for spatio-temporal super-resolution of turbulent flows
Direct numerical simulation (DNS) of turbulent flows is computationally expensive and cannot
be applied to flows with large Reynolds numbers. Low-resolution large eddy simulation (…
be applied to flows with large Reynolds numbers. Low-resolution large eddy simulation (…
Understanding data storage and ingestion for large-scale deep recommendation model training: Industrial product
Datacenter-scale AI training clusters consisting of thousands of domain-specific accelerators
(DSA) are used to train increasingly-complex deep learning models. These clusters rely on …
(DSA) are used to train increasingly-complex deep learning models. These clusters rely on …
Reconstructing Turbulent Flows Using Spatio-temporal Physical Dynamics
Accurate simulation of turbulent flows is of crucial importance in many branches of science
and engineering. Direct numerical simulation (DNS) provides the highest fidelity means of …
and engineering. Direct numerical simulation (DNS) provides the highest fidelity means of …
RM2Doc: A tool for automatic generation of requirements documents from requirements models
T Bao, J Yang, Y Yang, Y Yin�- Proceedings of the ACM/IEEE 44th�…, 2022 - dl.acm.org
Automatic generation of requirements documents is an essential feature of the model-driven
CASE tools such as UML and SysML designers. However, the quality of the generated …
CASE tools such as UML and SysML designers. However, the quality of the generated …
Modelling Physics-based Dynamic System using Machine Learning
T Bao - 2023 - ir.vanderbilt.edu
Partial differential equations (PDEs) have been widely used to describe a wide range of
phenomena such as fluid dynamics and quantum mechanics and classical numerical methods …
phenomena such as fluid dynamics and quantum mechanics and classical numerical methods …
A New Hybrid Automaton Framework with Partial Differential Equation Dynamics
This paper presents the syntax and semantics of a novel type of hybrid automaton (HA) with
partial differential equation (PDE) dynamic, partial differential hybrid automata (PDHA). In …
partial differential equation (PDE) dynamic, partial differential hybrid automata (PDHA). In …