466 citations · 945 across the 39 of their papers we have counts for
4 papers · 1 filter
Acceleration of horizontal numerical advection for atmospheric modeling through surrogate modeling with temporal coarse-graining
Manho Park, Christopher V. Rackauckas, Christopher W. Tessum
Machine-learned surrogate modeling of advection may accelerate geoscientific models, but existing approaches have either achieved limited speedup or have sacrificed spatial resolut…
Scientific Machine Learning-assisted Model Discovery from Telemetry Data
Sebastian Micluta-Campeanu, Avinash Subramanian, Anas Abdelrehim +4
Calibration of dynamic models to data is an important step in building building digital twins of HVAC equipment, thermal loads and control systems. Sometimes, when a model fails to…
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 Explicit Taylor ODE Integrators with Symbolic-Numeric Computing
Songchen Tan, Oscar Smith, Christopher Rackauckas
Taylor series methods show a newfound promise for the solution of non-stiff ordinary differential equations (ODEs) given the rise of new compiler-enhanced techniques for calculatin…