118 citations · 128 across the 6 of their papers we have counts for
6 papers
PDEInvBench: A Comprehensive Dataset and Design Space Exploration of Neural Networks for PDE Inverse Problems
Divyam Goel, Nithin Chalapathi, Sanjeev Raja +1
Inverse problems in partial differential equations (PDEs) involve estimating the physical parameters of a system from observed spatiotemporal solution fields. Neural networks are w…
Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels
Da Long, Wei W. Xing, Aditi S. Krishnapriyan +3
Discovering governing equations from data is important to many scientific and engineering applications. Despite promising successes, existing methods are still challenged by data s…
Learning differentiable solvers for systems with hard constraints
Geoffrey Négiar, Michael W. Mahoney, Aditi S. Krishnapriyan
We introduce a practical method to enforce partial differential equation (PDE) constraints for functions defined by neural networks (NNs), with a high degree of accuracy and up to…
AutoIP: A United Framework to Integrate Physics into Gaussian Processes
Da Long, Zheng Wang, Aditi Krishnapriyan +3
Physical modeling is critical for many modern science and engineering applications. From a data science or machine learning perspective, where more domain-agnostic, data-driven mod…
Learning continuous models for continuous physics
Aditi S. Krishnapriyan, Alejandro F. Queiruga, N. Benjamin Erichson +1
Dynamical systems that evolve continuously over time are ubiquitous throughout science and engineering. Machine learning (ML) provides data-driven approaches to model and predict t…
Characterizing possible failure modes in physics-informed neural networks
Aditi S. Krishnapriyan, Amir Gholami, Shandian Zhe +2
Recent work in scientific machine learning has developed so-called physics-informed neural network (PINN) models. The typical approach is to incorporate physical domain knowledge a…