3 papers
cs.LG2026
Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis
Lena Podina, Diba Darooneh, Joshveer Grewal +1
In engineering and applied mathematics, developing accurate mathematical models to predict and understand real-world phenomena is of utmost importance. Symbolic regression is a use…
q-bio.QM2026
Integrating Mechanistic Modeling and Machine Learning to Study CD4+/CD8+ CAR-T Cell Dynamics with Tumor Antigen Regulation
Saranya Varakunan, Melissa Stadt, Mohammad Kohandel
Chimeric antigen receptor (CAR) T cell therapy has shown remarkable success in hematological malignancies, yet patient responses remain highly variable and the roles of CD4+ and CD…
cs.LG2026
Private and interpretable clinical prediction with quantum-inspired tensor train models
José Ramón Pareja Monturiol, Juliette Sinnott, Roger G. Melko +1
Machine learning in clinical settings must balance predictive accuracy, interpretability, and privacy. Models such as logistic regression (LR) offer transparency, while neural netw…