Reversible and irreversible bracket-based dynamics for deep graph neural networks. - Anthony Gruber; Kookjin Lee; Nathaniel Trask (NeurIPS, 2023)

Canonical and Noncanonical Hamiltonian Operator Inference. - Anthony Gruber; Irina Tezaur (CoRR, 2023)

Domain Decomposition-based coupling of Operator Inference reduced order models via the Schwarz alternating method. - Ian Moore; Christopher R. Wentland; Anthony Gruber; Irina Tezaur (CoRR, 2024)

Gaussian Variational Schemes on Bounded and Unbounded Domains. - Jonas A. Actor; Anthony Gruber; Eric C. Cyr; Nathaniel Trask (CoRR, 2024)

MaD-Scientist: AI-based Scientist solving Convection-Diffusion-Reaction Equations Using Massive PINN-Based Prior Data. - Mingu Kang; Dongseok Lee; Woojin Cho; Jaehyeon Park; Kookjin Lee; Anthony Gruber; Youngjoon Hong; Noseong Park (CoRR, 2024)

Efficiently Parameterized Neural Metriplectic Systems. - Anthony Gruber; Kookjin Lee; Haksoo Lim; Noseong Park; Nathaniel Trask (ICLR, 2025)

Variationally Consistent Hamiltonian Model Reduction. - Anthony Gruber; Irina Tezaur (SIAM J. Appl. Dyn. Syst., 2025)

Understanding and Mitigating Membership Inference Risks of Neural Ordinary Differential Equations. - Sanghyun Hong 001; Fan Wu; Anthony Gruber; Kookjin Lee (CoRR, 2025)

Tensor parametric Hamiltonian operator inference. - Arjun Vijaywargiya; Shane A. McQuarrie; Anthony Gruber (CoRR, 2025)

Thermodynamically Consistent Latent Dynamics Identification for Parametric Systems. - Xiaolong He 001; Yeonjong Shin; Anthony Gruber; Sohyeon Jung; Kookjin Lee; Youngsoo Choi (CoRR, 2025)

Meta-learning Structure-Preserving Dynamics. - Cheng Jing; Uvini Balasuriya Mudiyanselage; Woojin Cho; Minju Jo; Anthony Gruber; Kookjin Lee (CoRR, 2025)