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- Sandia National LaboratoriesUS256 papers
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13 papers · 1 filter
Deep Learning without Global Optimization by Random Fourier Neural Networks
Owen Davis, Gianluca Geraci, Mohammad Motamed
We introduce a new training algorithm for deep neural networks that utilize random complex exponential activation functions. Our approach employs a Markov Chain Monte Carlo samplin…
Constrained or Unconstrained? Neural-Network-Based Equation Discovery from Data
Grant Norman, Jacqueline Wentz, Hemanth Kolla +2
Throughout many fields, practitioners often rely on differential equations to model systems. Yet, for many applications, the theoretical derivation of such equations and/or accurat…
Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes
Jeremiah Hauth, Cosmin Safta, Xun Huan +2
The application of neural network models to scientific machine learning tasks has proliferated in recent years. In particular, neural network models have proved to be adept at mode…
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…
Reversible and irreversible bracket-based dynamics for deep graph neural networks
Anthony Gruber, Kookjin Lee, Nathaniel Trask
Recent works have shown that physics-inspired architectures allow the training of deep graph neural networks (GNNs) without oversmoothing. The role of these physics is unclear, how…
Modular machine learning-based elastoplasticity: generalization in the context of limited data
Jan N. Fuhg, Craig M. Hamel, Kyle Johnson +2
The development of accurate constitutive models for materials that undergo path-dependent processes continues to be a complex challenge in computational solid mechanics. Challenges…