Universal Approximation of Input-Output Maps by Temporal Convolutional Nets. - Joshua Hanson; Maxim Raginsky (NeurIPS, 2019)

Universal Simulation of Stable Dynamical Systems by Recurrent Neural Nets. - Joshua Hanson; Maxim Raginsky (L4DC, 2020)

Learning Recurrent Neural Net Models of Nonlinear Systems. - Joshua Hanson; Maxim Raginsky; Eduardo D. Sontag (L4DC, 2021)

Learning compact physics-aware delayed photocurrent models using dynamic mode decomposition. - Joshua Hanson; Pavel B. Bochev; Biliana S. Paskaleva (Stat. Anal. Data Min., 2021)

Fitting an immersed submanifold to data via Sussmann's orbit theorem. - Joshua Hanson; Maxim Raginsky (CDC, 2022)

Rademacher complexity of neural ODEs via Chen-Fliess series. - Joshua Hanson; Maxim Raginsky (L4DC, 2024)

Constructing Noise-Robust Quantum Gates via Pontryagin's Maximum Principle. - Joshua Hanson; Dennis Lucarelli (QCE, 2024)

Non-intrusive data-driven model order reduction for circuits based on Hammerstein architectures. - Joshua Hanson; Biliana S. Paskaleva; Pavel B. Bochev (CoRR, 2024)

Tissue Characterization and Attenuation Mapping Using Multifrequency Ultrasound Imaging. - Joshua Hanson; Kenneth Hoyt (ISBI, 2025)

Expressivity of Quadratic Neural ODEs. - Joshua Hanson; Maxim Raginsky (CoRR, 2025)

Nonintrusive Data-Driven Model Order Reduction for Circuits Based on Hammerstein Architectures. - Joshua Hanson; Paul Kuberry; Biliana S. Paskaleva; Pavel B. Bochev (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2025)