adaptive treatment scheduling 1brain tumor modeling 1digital twin 1model predictive control 1reaction-diffusion 1
From the 1 of 3 linked papers with an AI index.
3 papers
cs.LG2026
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…
cs.LG2026
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…
cs.LG2026
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…