2 citations · 6 across the 8 of their papers we have counts for
8 papers
eIRWR: Enhanced Iterative Random Walk with Restart for Scalable Root Cause Analysis in Microservices
Saiful Khan, Afrah Farea
Root cause analysis (RCA) in microservice architectures needs to pinpoint the originating faulty service responsible for the cascading symptoms seen across hundreds or thousands of…
Understanding How Humans Inject Knowledge into Machine Learning Workflows through Visual Analytics
Yiwen Xing, Philip Beaucamp, Joyraj Chakraborty +6
Visual analytics (VA) plays an increasingly important role in supporting machine learning (ML) workflows. In the field of visualization, such approaches and techniques are referred…
A Multiplexing Design Space: Theory, Method, and Application
Yiwen Xing, Afrah Farea, Saiful Khan +1
Many visualization designs feature phenomena referred to as ``visual multiplexing'', where multiple pieces of information associated with the same data point are conveyed simultane…
Multi-Objective Loss Balancing in Physics-Informed Neural Networks for Fluid Flow Applications
Afrah Farea, Saiful Khan, Mustafa Serdar Celebi
Physics-Informed Neural Networks (PINNs) have emerged as a promising machine learning approach for solving partial differential equations (PDEs). However, PINNs face significant ch…
Learning Fluid-Structure Interaction with Physics-Informed Machine Learning and Immersed Boundary Methods
Afrah Farea, Saiful Khan, Reza Daryani +2
Physics-informed neural networks (PINNs) have emerged as a promising approach for solving complex fluid dynamics problems, yet their application to fluid-structure interaction (FSI…
QCPINN: Quantum-Classical Physics-Informed Neural Networks for Solving PDEs
Afrah Farea, Saiful Khan, Mustafa Serdar Celebi
Physics-informed neural networks (PINNs) have emerged as promising methods for solving partial differential equations (PDEs) by embedding physical laws within neural architectures.…
Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations
Afrah Farea, Mustafa Serdar Celebi
Physics-Informed Neural Networks (PINNs) have emerged as a promising approach for solving Partial Differential Equations (PDEs). However, they face challenges related to spectral b…
Exponential Quantum Speedup for Simulation-Based Optimization Applications
Jonas Stein, Lukas Müller, Leonhard Hölscher +10
The simulation of many industrially relevant physical processes can be executed up to exponentially faster using quantum algorithms. However, this speedup can only be leveraged if…