5 papers
Physics-Informed Deep B-Spline Networks
Zhuoyuan Wang, Raffaele Romagnoli, Saviz Mowlavi +1
Physics-informed machine learning offers a promising framework for solving complex partial differential equations (PDEs) by integrating observational data with governing physical l…
Stability Analysis of a B-Spline Deep Neural Operator for Nonlinear Systems
Raffaele Romagnoli, Soummya Kar
This paper investigates the stability properties of neural operators through the structured representation offered by the Hybrid B-spline Deep Neural Operator (HBDNO). While existi…
Neural Spline Operators for Risk Quantification in Stochastic Systems
Zhuoyuan Wang, Raffaele Romagnoli, Kamyar Azizzadenesheli +1
Accurately quantifying long-term risk probabilities in diverse stochastic systems is essential for safety-critical control. However, existing sampling-based and partial differentia…
Safety-Aware Multi-Agent Learning for Dynamic Network Bridging
Raffaele Galliera, Konstantinos Mitsopoulos, Niranjan Suri +1
Addressing complex cooperative tasks in safety-critical environments poses significant challenges for multi-agent systems, especially under conditions of partial observability. We…
Control-Oriented Models Inform Synthetic Biology Strategies in CAR T Cell Immunotherapy
Raffaele Romagnoli
Chimeric antigen receptor (CAR) T cell therapy is revolutionizing the treatment of blood cancers. Mathematical models that can predict the effectiveness of immunotherapies such as…