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- Sandia National Laboratories CaliforniaUS80 papers
- Center for Integrated NanotechnologiesUS28 papers
- Los Alamos National LaboratoryUS25 papers
- University of New MexicoUS20 papers
- Oak Ridge National LaboratoryUS15 papers
- Princeton UniversityUS14 papers
- Lawrence Livermore National LaboratoryUS12 papers
- The University of Texas at AustinUS12 papers
- Lawrence Berkeley National LaboratoryUS11 papers
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- Argonne National LaboratoryUS10 papers
- Georgia Institute of TechnologyUS10 papers
9 papers · 1 filter
Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data
Wai Tong Chung, Bassem Akoush, Pushan Sharma +9
Analysis of compressible turbulent flows is essential for applications related to propulsion, energy generation, and the environment. Here, we present BLASTNet 2.0, a 2.2 TB networ…
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Megan M. Baker, Alexander New, Mario Aguilar-Simon +44
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks enc…
Polynomial-Spline Neural Networks with Exact Integrals
Jonas A. Actor, Andy Huang, Nathaniel Trask
Using neural networks to solve variational problems, and other scientific machine learning tasks, has been limited by a lack of consistency and an inability to exactly integrate ex…
Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling
Kookjin Lee, Nathaniel Trask, Panos Stinis
Discovery of dynamical systems from data forms the foundation for data-driven modeling and recently, structure-preserving geometric perspectives have been shown to provide improved…
Probabilistic partition of unity networks: clustering based deep approximation
Nat Trask, Mamikon Gulian, Andy Huang +1
Partition of unity networks (POU-Nets) have been shown capable of realizing algebraic convergence rates for regression and solution of PDEs, but require empirical tuning of trainin…
Partition of unity networks: deep hp-approximation
Kookjin Lee, Nathaniel A. Trask, Ravi G. Patel +2
Approximation theorists have established best-in-class optimal approximation rates of deep neural networks by utilizing their ability to simultaneously emulate partitions of unity…