5 citations · 5 across the 4 of their papers we have counts for
4 papers
Probabilistic Neural Networks (PNNs) for Modeling Aleatoric Uncertainty in Scientific Machine Learning
Farhad Pourkamali-Anaraki, Jamal F. Husseini, Scott E. Stapleton
This paper investigates the use of probabilistic neural networks (PNNs) to model aleatoric uncertainty, which refers to the inherent variability in the input-output relationships o…
Two-Stage Surrogate Modeling for Data-Driven Design Optimization with Application to Composite Microstructure Generation
Farhad Pourkamali-Anaraki, Jamal F. Husseini, Evan J. Pineda +2
This paper introduces a novel two-stage machine learning-based surrogate modeling framework to address inverse problems in scientific and engineering fields. In the first stage of…
D-CBRS: Accounting For Intra-Class Diversity in Continual Learning
Yasin Findik, Farhad Pourkamali-Anaraki
Continual learning -- accumulating knowledge from a sequence of learning experiences -- is an important yet challenging problem. In this paradigm, the model's performance for previ…
Randomized Clustered Nystrom for Large-Scale Kernel Machines
Farhad Pourkamali-Anaraki, Stephen Becker
The Nystrom method has been popular for generating the low-rank approximation of kernel matrices that arise in many machine learning problems. The approximation quality of the Nyst…