79 citations · 104 across the 8 of their papers we have counts for
4 papers · 1 filter
Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs
S. Chandra Mouli, Danielle C. Maddix, Shima Alizadeh +4
Existing work in scientific machine learning (SciML) has shown that data-driven learning of solution operators can provide a fast approximate alternative to classical numerical par…
Pessimistic Off-Policy Multi-Objective Optimization
Shima Alizadeh, Aniruddha Bhargava, Karthick Gopalswamy +3
Multi-objective optimization is a type of decision making problems where multiple conflicting objectives are optimized. We study offline optimization of multi-objective policies fr…
Learning Physical Models that Can Respect Conservation Laws
Derek Hansen, Danielle C. Maddix, Shima Alizadeh +2
Recent work in scientific machine learning (SciML) has focused on incorporating partial differential equation (PDE) information into the learning process. Much of this work has foc…
Guiding continuous operator learning through Physics-based boundary constraints
Nadim Saad, Gaurav Gupta, Shima Alizadeh +1
Boundary conditions (BCs) are important groups of physics-enforced constraints that are necessary for solutions of Partial Differential Equations (PDEs) to satisfy at specific spat…