3 papers
cs.LG2025
Probabilistic Neural Networks (PNNs) with t-Distributed Outputs: Adaptive Prediction Intervals Beyond Gaussian Assumptions
Farhad Pourkamali-Anaraki
Traditional neural network regression models provide only point estimates, failing to capture predictive uncertainty. Probabilistic neural networks (PNNs) address this limitation b…
stat.ML2024
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…
cs.LG2024
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…