Ali Forootani
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Climate Aware Deep Neural Networks (CADNN) for wind power simulation Open
Wind power forecasting plays a vital role in modern energy systems by facilitating the integration of renewable energy sources into the power grid. Accurate prediction of wind energy output is essential for managing the inherent intermitte…
DeepKoopFormer: A Koopman Enhanced Transformer Based Architecture for Time Series Forecasting Open
Time series forecasting plays a vital role across scientific, industrial, and environmental domains, especially when dealing with high-dimensional and nonlinear systems. While Transformer-based models have recently achieved state-of-the-ar…
Synthetic Time Series Forecasting with Transformer Architectures: Extensive Simulation Benchmarks Open
Time series forecasting plays a critical role in domains such as energy, finance, and healthcare, where accurate predictions inform decision-making under uncertainty. Although Transformer-based models have demonstrated success in sequentia…
View article: Off-Policy Temporal Difference Learning for Perturbed Markov Decision Processes: Theoretical Insights and Extensive Simulations
Off-Policy Temporal Difference Learning for Perturbed Markov Decision Processes: Theoretical Insights and Extensive Simulations Open
Dynamic Programming suffers from the curse of dimensionality due to large state and action spaces, a challenge further compounded by uncertainties in the environment. To mitigate these issue, we explore an off-policy based Temporal Differe…
GS-PINN: Greedy Sampling for Parameter Estimation in Partial Differential Equations Open
Partial differential equation parameter estimation is a mathematical and computational process used to estimate the unknown parameters in a partial differential equation model from observational data. This paper employs a greedy sampling a…