7 citations · 8 across the 6 of their papers we have counts for
6 papers
ABM-UDE: Developing Surrogates for Epidemic Agent-Based Models via Scientific Machine Learning
Sharv Murgai, Utkarsh Utkarsh, Kyle C. Nguyen +3
Agent-based epidemic models (ABMs) encode behavioral and policy heterogeneity but are too slow for nightly hospital planning. We develop county-ready surrogates that learn directly…
Efficient Symbolic Computation via Hash Consing
Bowen Zhu, Aayush Sabharwal, Songchen Tan +3
Symbolic computation systems suffer from memory inefficiencies due to redundant storage of structurally identical subexpressions, commonly known as expression swell, which degrades…
Scientific Machine Learning of Chaotic Systems Learns Reduced-Order Equations for Neural Populations
Anthony G. Chesebro, David Hofmann, Vaibhav Dixit +6
Extracting interpretable mathematical models from complex dynamical systems is difficult, especially for chaotic dynamics observed with noisy experimental data. We present PEM-UDE,…
Semi-Explicit Neural DAEs: Learning Long-Horizon Dynamical Systems with Algebraic Constraints
Avik Pal, Alan Edelman, Christopher Rackauckas
Despite the promise of scientific machine learning (SciML) in combining data-driven techniques with mechanistic modeling, existing approaches for incorporating hard constraints in…
Scalable higher-order nonlinear solvers via higher-order automatic differentiation
Songchen Tan, Keming Miao, Alan Edelman +1
This paper demonstrates new methods and implementations of nonlinear solvers with higher-order of convergence, which is achieved by efficiently computing higher-order derivatives.…
NonlinearSolve.jl: High-Performance and Robust Solvers for Systems of Nonlinear Equations in Julia
Avik Pal, Flemming Holtorf, Axel Larsson +6
Efficiently solving nonlinear equations underpins numerous scientific and engineering disciplines, yet scaling these solutions for challenging system models remains a challenge. Th…