5 papers
PG-KINN: A Physics-Informed Petrov-Galerkin Kolmogorov-Arnold Network for Solving Forward and Inverse PDEs
Amirhossein Sadr, Nima Soltani, Vahideh Moghtadaiee +3
Physics-informed learning of partial differential equations (PDEs) has been dominated by multilayer perceptrons (MLPs), whose spectral bias and dense parameterization limit both ac…
A Generic Modulo- RNS Multiplier Based on Twit Representation
Saeid Gorgin, Amirhossein Sadr, Behzad Salami +1
Modular multiplication is a fundamental arithmetic primitive in Residue Number Systems (RNS) and is often the dominant source of delay, area, and energy consumption in RNS datapath…
ITS-Mina: A Harris Hawks Optimization-Based All-MLP Framework with Iterative Refinement and External Attention for Multivariate Time Series Forecasting
Pourya Zamanvaziri, Amirhossein Sadr, Aida Pakniyat +1
Multivariate time series forecasting plays a pivotal role in numerous real-world applications, including financial analysis, energy management, and traffic planning. While Transfor…
Modeling and Simulation Frameworks for Processing-in-Memory Architectures
Mahdi Aghaei, Saba Ebrahimi, Mohammad Saleh Arafati +4
Processing-in-Memory (PIM) has emerged as a promising computing paradigm to address the memory wall and the fundamental bottleneck of the von Neumann architecture by reducing costl…
GraphDerm: Fusing Imaging, Physical Scale, and Metadata in a Population-Graph Classifier for Dermoscopic Lesions
Mehdi Yousefzadeh, Parsa Esfahanian, Sara Rashidifar +5
Introduction. Dermoscopy aids melanoma triage, yet image-only AI often ignores patient metadata (age, sex, site) and the physical scale needed for geometric analysis. We present Gr…