9 citations · 9 across the 7 of their papers we have counts for
7 papers
Integrating Wide and Deep Neural Networks with Squeeze-and-Excitation Blocks for Multi-Target Property Prediction in Additively Manufactured Fiber Reinforced Composites
Behzad Parvaresh, Rahmat K. Adesunkanmi, Adel Alaeddini
Continuous fiber-reinforced composite manufactured by additive manufacturing (CFRC-AM) offers opportunities for printing lightweight materials with high specific strength. However,…
Operator-Theoretic Joint Estimation of Aging-Aware State of Charge and Control-Informed State of Health
Rahmat K. Adesunkanmi, Adel Alaeddini, Mahesh Krishnamurthy
Accurate estimation of a battery's state of charge and state of health is essential for safe and reliable battery management. Existing approaches often decouple these two states, l…
Root Cause Analysis of Hydrogen Bond Separation in Spatio-Temporal Molecular Dynamics using Causal Models
Rahmat K. Adesunkanmi, Ashfaq Khokhar, Goce Trajcevski +1
Molecular dynamics simulations (MDS) face challenges, including resource-heavy computations and the need to manually scan outputs to detect "interesting events," such as the format…
Multi-Modal Drift Forecasting of Leeway Objects via Navier-Stokes-Guided CNN and Sequence-to-Sequence Attention-Based Models
Rahmat K. Adesunkanmi, Alexander W. Brandt, Masoud Deylami +3
Accurately predicting the drift (displacement) of leeway objects in maritime environments remains a critical challenge, particularly in time-sensitive scenarios such as search and…
NeuroKoopman Dynamic Causal Discovery
Rahmat Adesunkanmi, Balaji Sesha Srikanth Pokuri, Ratnesh Kumar
In many real-world applications where the system dynamics has an underlying interdependency among its variables (such as power grid, economics, neuroscience, omics networks, enviro…
Data-Driven Linear Koopman Embedding for Networked Systems: Model-Predictive Grid Control
Ramij R. Hossain, Rahmat Adesunkanmi, Ratnesh Kumar
This paper presents a data-learned linear Koopman embedding of nonlinear networked dynamics and uses it to enable real-time model predictive emergency voltage control in a power ne…
Expectation Distance-based Distributional Clustering for Noise-Robustness
Rahmat Adesunkanmi, Ratnesh Kumar
This paper presents a clustering technique that reduces the susceptibility to data noise by learning and clustering the data-distribution and then assigning the data to the cluster…