From the 1 of 5 linked papers with an AI index.
5 papers
AI-Augmented Adaptive Digital Twin Modeling for Brain Tumor Evolution Prediction and Treatment Scheduling
Wenxi Liu, Michael Trimboli, Xianqi Li
The paper presents an AI‑augmented digital twin framework that combines a reaction‑diffusion model with a 3D residual learning module to predict brain tumor growth and to optimize…
Physics-Guided Fully Convolutional Spatiotemporal Learning Toward Digital-Twin-Enabled Microstructure Evolution Prediction
Michael Trimboli, Wenxi Liu, Xianqi Li
Understanding and predicting microstructure evolution is central to materials design, yet purely data-driven spatiotemporal learning models often suffer from limited physical consi…
Fully Convolutional Spatiotemporal Learning for Microstructure Evolution Prediction
Michael Trimboli, Mohammed Alsubaie, Sirani M. Perera +2
Understanding and predicting microstructure evolution is fundamental to materials science, as it governs the resulting properties and performance of materials. Traditional simulati…
A Low-complexity Structured Neural Network to Realize States of Dynamical Systems
Hansaka Aluvihare, Levi Lingsch, Xianqi Li +1
Data-driven learning is rapidly evolving and places a new perspective on realizing state-space dynamical systems. However, dynamical systems derived from nonlinear ordinary differe…
A Low-complexity Structured Neural Network Approach to Intelligently Realize Wideband Multi-beam Beamformers
Hansaka Aluvihare, Sivakumar Sivasankar, Xianqi Li +2
True-time-delay (TTD) beamformers can produce wideband, squint-free beams in both analog and digital signal domains, unlike frequency-dependent FFT beams. Our previous work showed…