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 …

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 …

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, …

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 …

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 (…

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 …

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 …

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 …

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 …

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 …