8 papers
Pi-transformer: A prior-informed dual-attention model for multivariate time-series anomaly detection
Sepehr Maleki, Negar Pourmoazemi
Anomalies in multivariate time series often arise from temporal context and cross-channel coordination rather than isolated outliers. We present Pi-Transformer (Prior-Informed Tran…
Capacity-constrained demand response in smart grids using deep reinforcement learning
Shafagh Abband Pashaki, Sepehr Maleki, Amir Badiee
This paper presents a capacity-constrained incentive-based demand response approach for residential smart grids. It aims to maintain electricity grid capacity limits and prevent co…
FoilDiff: A Hybrid Transformer Backbone for Diffusion-based Modelling of 2D Airfoil Flow Fields
Kenechukwu Ogbuagu, Sepehr Maleki, Giuseppe Bruni +1
The accurate prediction of flow fields around airfoils is crucial for aerodynamic design and optimisation. Computational Fluid Dynamics (CFD) models are effective but computational…
Diffusion Models: A Mathematical Introduction
Sepehr Maleki, Negar Pourmoazemi
We present a concise, self-contained derivation of diffusion-based generative models. Starting from basic properties of Gaussian distributions (densities, quadratic expectations, r…
Physics-Informed Neural Networks for Industrial Gas Turbines: Recent Trends, Advancements and Challenges
Afila Ajithkumar Sophiya, Sepehr Maleki, Giuseppe Bruni +1
Physics-Informed Neural Networks (PINNs) have emerged as a promising computational framework for solving differential equations by integrating deep learning with physical constrain…
A comprehensive analysis of PINNs: Variants, Applications, and Challenges
Afila Ajithkumar Sophiya, Akarsh K Nair, Sepehr Maleki +1
Physics Informed Neural Networks (PINNs) have been emerging as a powerful computational tool for solving differential equations. However, the applicability of these models is still…