14 papers
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…
The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)
Andrew Ferguson, Marisa LaFleur, Lars Ruthotto +97
This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 w…
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…
Physics-Constrained Flow Matching: Sampling Generative Models with Hard Constraints
Utkarsh Utkarsh, Pengfei Cai, Alan Edelman +2
Deep generative models have recently been applied to physical systems governed by partial differential equations (PDEs), offering scalable simulation and uncertainty-aware inferenc…