2 citations · 4 across the 10 of their papers we have counts for
6 papers · 1 filter
Domain-Decomposed Graph Neural Network Surrogate Modeling for Ice Sheets
Adrienne M. Propp, Mauro Perego, Eric C. Cyr +5
Accurate yet efficient surrogate models are essential for large-scale simulations of partial differential equations (PDEs), particularly for uncertainty quantification (UQ) tasks t…
Deriving Transformer Architectures as Implicit Multinomial Regression
Jonas A. Actor, Anthony Gruber, Eric C. Cyr
While attention has been empirically shown to improve model performance, it lacks a rigorous mathematical justification. This short paper establishes a novel connection between att…
Meta-learning Structure-Preserving Dynamics
Cheng Jing, Uvini Balasuriya Mudiyanselage, Woojin Cho +3
Structure-preserving approaches to dynamics discovery have demonstrated great potential for modeling physical systems due to their use of strong inductive biases, which enforce key…
Thermodynamically Consistent Latent Dynamics Identification for Parametric Systems
Xiaolong He, Yeonjong Shin, Anthony Gruber +3
We propose an efficient thermodynamics-informed latent space dynamics identification (tLaSDI) framework for the reduced-order modeling of parametric nonlinear dynamical systems. Th…
Tensor parametric Hamiltonian operator inference
Arjun Vijaywargiya, Shane A. McQuarrie, Anthony Gruber
This work presents a tensorial approach to constructing data-driven reduced-order models corresponding to semi-discrete partial differential equations with canonical Hamiltonian st…
Modeling Neural Networks with Privacy Using Neural Stochastic Differential Equations
Sanghyun Hong, Fan Wu, Anthony Gruber +1
In this work, we study the feasibility of using neural ordinary differential equations (NODEs) to model systems with intrinsic privacy properties. Unlike conventional feedforward n…